title: Medina

text: Eventually the Banu 'Aws and the Banu Khazraj became hostile to each other and by the time of Muhammad's Hijrah (emigration) to Medina in 622, they had been fighting for 120 years and were sworn enemies The Banu Nadir and the Banu Qurayza were allied with the 'Aws, while the Banu Qaynuqa sided with the Khazraj. They fought a total of four wars.


title: Medina

text: Their last and bloodiest known battle was the Battle of Bu'ath, fought a few years prior to the arrival of Muhammad. The outcome of the battle was inconclusive, and the feud continued. 'Abd Allah ibn Ubayy, one Khazraj chief, had refused to take part in the battle, which earned him a reputation for equity and peacefulness. He was the most respected inhabitant of the city prior to Muhammad's arrival. To solve the ongoing feud, concerned residents of Yathrib met secretly with Muhammad in 'Aqaba, a place outside Mecca, inviting him and his small group of believers to come to the city, where Muhammad could serve a mediator between the factions and his community could practice its faith freely.


title: Medina

text: In 622, Muhammad and an estimated 70 Meccan Muhajirun left Mecca over a period of a few months for sanctuary in Yathrib, an event that transformed the religious and political landscape of the city completely; the longstanding enmity between the Aus and Khazraj tribes was dampened as many of the two Arab tribes and some local Jews embraced the new religion of Islam. Muhammad, linked to the Khazraj through his great-grandmother, was agreed on as the leader of the city. The natives of Yathrib who had converted to Islam of any background—pagan Arab or Jewish—were called the "Ansar" ("the Patrons" or "the Helpers"), while the Muslims would pay the Zakat tax.


title: Medina

text: According to Ibn Ishaq, all parties in the area agreed to the Constitution of Medina, which committed all parties to mutual cooperation under the leadership of Muhammad. The nature of this document as recorded by Ibn Ishaq and transmitted by Ibn Hisham is the subject of dispute among modern Western historians, many of whom maintain that this "treaty" is possibly a collage of different agreements, oral rather than written, of different dates, and that it is not clear exactly when they were made. Other scholars, however, both Western and Muslim, argue that the text of the agreement—whether a single document originally or several—is possibly one of the oldest Islamic texts we possess. In Yemenite Jewish sources, another treaty was drafted between Muhammad and his Jewish subjects, known as "Kitāb Dimmat al-Nabi", written in the 3rd year of the Hijra (625), and which gave express liberty to Jews living in Arabia to observe the Sabbath and to grow-out their side-locks. In return, they were to pay the jizya annually for protection by their patrons.


title: Medina

text: In the year 625, Abu Sufyan ibn Harb, a senior chieftain of Mecca who later converted to Islam, led a Meccan force against Medina. Muhammad marched out to meet the Qurayshi army with an estimated 1,000 troops, but just as the army approached the battlefield, 300 men under 'Abd Allah ibn Ubayy withdrew, dealing a severe blow to the Muslim army's morale. Muhammad continued marching with his now 700-strong force and ordered a group of 50 archers to climb a small hill, now called "Jabal ar-Rummaah" (The Archers' Hill) to keep an eye on the Meccan's cavalry and to provide protection to the rear of the Muslim's army. As the battle heated up, the Meccans were forced to retreat. The frontline was pushed further and further away from the archers and foreseeing the battle to be a victory for the Muslims, the archers decided to leave their posts to pursue the retreating Meccans. A small party, however, stayed behind; pleading the rest to not disobey Muhammad's orders.


title: Medina

text: Seeing that the archers were starting to descend from the hill, Khalid ibn al-Walid commanded his unit to ambush the hill and his cavalry unit pursued the descending archers were systematically slain by being caught in the plain ahead of the hill and the frontline, watched upon by their desperate comrades who stayed behind up in the hill who were shooting arrows to thwart the raiders, but with little to no effect. However, the Meccans did not capitalize on their advantage by invading Medina and returned to Mecca. The Madanis (people of Medina) suffered heavy losses, and Muhammad was injured.


title: Medina

text: In 627, Abu Sufyan led another force toward Medina. Knowing of his intentions, Muhammad asked for proposals for defending the northern flank of the city, as the east and west were protected by volcanic rocks and the south was covered with palm trees. Salman al-Farsi, a Persian Sahabi who was familiar with Sasanian war tactics recommended digging a trench to protect the city and Muhammad accepted it. The subsequent siege came to be known as the Battle of the Trench and the Battle of the Confederates. After a month-long siege and various skirmishes, the Meccans withdrew again due to the harsh winter.


title: Medina

text: During the siege, Abu Sufyan contacted the Jewish tribe of Banu Qurayza and formed an agreement with them, to attack the Muslim defenders and effectively encircle the defenders. It was however discovered by the Muslims and thwarted. This was in breach of the Constitution of Medina and after the Meccan withdrawal, Muhammad immediately marched against the Qurayza and laid siege to their strongholds. The Jewish forces eventually surrendered. Some members of the Aws negotiated on behalf of their old allies and Muhammad agreed to appoint one of their chiefs who had converted to Islam, Sa'd ibn Mu'adh, as judge. Sa'ad judged by Jewish law that all male members of the tribe should be killed and the women and children enslaved as was the law stated in the Old Testament for treason in the Book of Deutoronomy. This action was conceived of as a defensive measure to ensure that the Muslim community could be confident of its continued survival in Medina. The French historian Robert Mantran proposes that from this point of view it was successful—from this point on, the Muslims were no longer primarily concerned with survival but with expansion and conquest.


title: Medina

text: In the ten years following the hijra, Medina formed the base from which Muhammad and the Muslim army attacked and were attacked, and it was from here that he marched on Mecca, entering it without battle in 630. Despite Muhammad's tribal connection to Mecca, the growing importance of Mecca in Islam, the significance of the Ka'bah as the center of the Islamic world, as the direction of prayer (Qibla), and in the Islamic pilgrimage (Hajj), Muhammad returned to Medina, which remained for some years the most important city of Islam and the base of operations of the early Rashidun Caliphate.


title: Medina

text: The city is presumed to have been renamed "Madinat al-Nabi" ("City of the Prophet" in Arabic) in honor of Muhammad's prophethood and the city being the site of his burial. Alternatively, Lucien Gubbay suggests the name Medina could also have been a derivative from the Aramaic word "Medinta", which the Jewish inhabitants could have used for the city.


title: Medina

text: Under the first three caliphs Abu Bakr, Umar, and Uthman, Medina was the capital of a rapidly increasing Muslim Empire. During the reign of 'Uthman ibn al-Affan, the third caliph, a party of Arabs from Egypt, disgruntled at some of his political decisions, attacked Medina in 656 and assassinated him in his own home. Ali, the fourth caliph, changed the capital of the caliphate from Medina to Kufa in Iraq for being in a more strategic location. Since then, Medina's importance dwindled, becoming more a place of religious importance than of political power. Medina witnessed little to no economic growth during and after Ali's reign.


title: Medina

text: After al-Hasan, the son of 'Ali, ceded power to Mu'awiyah I, son of Abu Sufyan, Mu'awiyah marched into Kufa, Ali's capital, and received the allegiance of the local 'Iraqis. This is considered to be the beginning of the Umayyad caliphate. Mu'awiyah's governors took special care of Medina and dug the 'Ayn az-Zarqa'a ("Blue Spring") spring along with a project that included the creation of underground ducts for the purposes of irrigation. Dams were built in some of the wadis and the subsequent agricultural boom led to the strengthening of the economy.


title: Medina

text: Following a period of unrest during the Second Fitna in 679, Husayn ibn 'Ali was martyred at Karbala and Yazid assumed unchecked control for the next three years. In 682, Abd Allah ibn al-Zubayr declared himself Caliph of Mecca and the people of Medina swore allegiance to him. This led to an eight-year-long period of economic distress for the city. In 692, the Umayyads regained power and Medina experienced its second period of huge economic growth. Trade improved and more people moved into the city. The banks of Wadi al-'Aqiq were now lush with greenery. This period of peace and prosperity coincided with the rule of 'Umar ibn Abdulaziz, who many consider to be the fifth of the Rashidun.


title: Medina

text: Abdulbasit A. Badr, in his book, "Madinah, The Enlightened City: History and Landmarks", divides this period into three distinct phases:


title: Medina

text: Badr describes the period between 749 and 974 as a push-and-pull between peace and political turmoil, while Medina continued to pay allegiance to the Abbasids. From 974 to 1151, Medina was in a liaison with the Fatimids, even though the political stand between the two remained turbulent and did not exceed the normal allegiance. From 1151 onwards, Medina paid allegiance to the Zengids, and the Emir Nuruddin Zangi took care of the roads used by pilgrims and funded the fixing of the water sources and streets. When he visited Medina in 1162, he ordered the construction of a new wall that encompassed the new urban areas outside the old city wall. Zangi was succeeded by Salahuddin al-Ayyubi, founder of the Ayyubid dynasty, who supported Qasim ibn Muhanna, the Governor of Medina, and greatly funded the growth of the city while slashing taxes paid by the pilgrims. He also funded the Bedouins who lived on the routes used by pilgrims to protect them on their journeys. The later Abbasids also continued to fund the expenses of the city. While Medina was formally allied with the Abbasids during this period, they maintained closer relations with the Zengids and Ayyubids. The historic city formed an oval, surrounded by a strong wall, high, dating from this period, and was flanked with towers. Of its four gates, the "Bab al-Salam" ("The Gate of Peace"), was remarked for its beauty. Beyond the walls of the city, the west and south were suburbs consisting of low houses, yards, gardens and plantations.


title: Medina

text: After a brutal long conflict with the Abbasids, the Mamluk Sultanate of Cairo took over the Egyptian governorate and effectively gained control of Medina. In 1256, Medina was threatened by lava from the Harrat Rahat volcanic region but was narrowly saved from being burnt after the lava turned northward. During Mamluk reign, the Masjid an-Nabawi caught fire twice. Once in 1256, when the storage caught fire, burning the entire mosque, and the other time in 1481, when the masjid was struck by lightning. This period also coincided with an increase in scholarly activity in Medina, with scholars such as Ibn Farhun, Al-Hafiz Zain al-Din al-'Iraqi, Al Sakhawi and others settling in the city. The striking iconic Green Dome also found its beginnings as a cupola built under Mamluk Sultan al-Mansur Qalawun as-Salihi in 1297.


title: Medina

text: In 1517, the first Ottoman period began with Selim I's conquest of Mamluk Egypt. This added Medina to their territory and they continued the tradition of showering Medina with money and aid. In 1532, Suleiman the Magnificent built a secure fortress around the city and constructed a strong castle armed by an Ottoman battalion to protect the city. This is also the period in which many of the Prophet's Mosque's modern features were built even though it wasn't painted green yet. These suburbs also had walls and gates. The Ottoman sultans took a keen interest in the Prophet's Mosque and redesigned it over and over to suit their preferences.


title: Medina

text: As the Ottomans' hold over their domains broke loose, the Madanis pledged alliance to Saud bin Abdulaziz, founder of the First Saudi state in 1805, who quickly took over the city. In 1811, Muhammad Ali Pasha, Ottoman commander and Wali of Egypt, commanded two armies under each of his two sons to seize Medina, the first one, under the elder Towson Pasha, failed to take Medina. But the second one, a larger army under the command of Ibrahim Pasha, succeeded after battling a fierce resistance movement.


title: Medina

text: After defeating his Saudi foes, Muhammad Ali Pasha took over governance of Medina and although he did not formally declare independence, his governance took on more of a semi-autonomous style. Muhammad's sons, Towson and Ibrahim, alternated in the governance of the city. Ibrahim renovated the city's walls and the Prophet's Mosque. He established a grand provision distribution center (taqiyya) to distribute food and alms to the needy and Medina lived a period of security and peace. In 1840, Muhammad moved his troops out of the city and officially handed the city to the central Ottoman command.


title: Medina

text: Four years in 1844, after Muhammad Ali Pasha's departure, Davud Pasha was given the position of governor of Medina under the Ottoman sultan. Davud was responsible for renovating the Prophet's Mosque on Sultan Abdulmejid I's orders. When Abdul Hamid II assumed power, he made Medina stand out of the desert with a number of modern marvels, including a radio communication station, a power plant for the Prophet's Mosque and its immediate vicinity, a telegraph line between Medina and Istanbul, and the Hejaz railway which ran from Damascus to Medina with a planned extension to Mecca. Within one decade, the population of the city multiplied by leaps and bounds and reached 80,000. Around this time, Medina started falling prey to a new threat, the Hashemite Sharifate of Mecca in the south. Medina witnessed the longest siege in its history during and after World War I.


title: Medina

text: The Sharif of Mecca, Husayn ibn Ali, first attacked Medina on 6 June 1916, in the middle of World War I. Four days later, Husayn held Medina in a bitter 3-year siege, during which the people faced food shortages, widespread disease and mass emigration. Fakhri Pasha, governor of Medina, tenaciously held on during the Siege of Medina from 10 June 1916 and refused to surrender and held on another 72 days after the Armistice of Moudros, until he was arrested by his own men and the city was taken over by the Sharifate on 10 January 1919. Husayn largely won the war due to his alliance with the British. In anticipation of the plunder and destruction to follow, Fakhri Pasha secretly dispatched the Sacred Relics of Muhammad to the Ottoman capital, Istanbul. As of 1920, the British described Medina as "much more self-supporting than Mecca." After the Great War, the Sharif of Mecca, Sayyid Hussein bin Ali was proclaimed King of an independent Hejaz. Soon after, the people of Medina secretly entered an agreement with Ibn Saud in 1924, and his son, Prince Mohammed bin Abdulaziz conquered Medina as part of the Saudi conquest of Hejaz on 5 December 1925 which gave way to the whole of the Hejaz being incorporated into the modern Kingdom of Saudi Arabia.


title: Medina

text: The Kingdom of Saudi Arabia focused more on the expansion of the city and the demolition of former sites that violated Islamic principles and Islamic law such as the tombs at al-Baqi. Nowadays, the city mostly only holds religious significance and as such, just like Mecca, has given rise to a number of hotels surrounding the Al-Masjid an-Nabawi, which unlike the Masjid Al-Ḥarām, is equipped with an underground parking. The old city's walls have been destroyed and replaced with the three ring roads that encircle Medina today, named in order of length, King Faisal Road, King Abdullah Road and King Khalid Road. Medina's ring roads generally see less traffic overall compared to the four ring roads of Mecca.


title: Medina

text: An international airport, named the Prince Mohammed Bin Abdulaziz International Airport, now serves the city and is located on Highway 340, known locally as the Old Qassim Road. The city now sits at the crossroads of two major Saudi Arabian highways, Highway 60, known as the Qassim–Medina Highway, and Highway 15 which connects the city to Mecca in the south and onward and Tabuk in the north and onward, known as the Al Hijrah Highway or Al Hijrah Road, after Muhammad's journey. The old Ottoman railway system was shutdown after their departure from the region and the old railway station has now been converted into a museum. The city has recently seen another connection and mode of transport between it and Mecca, the Haramain high-speed railway line connects the two cities via King Abdullah Economic City near Rabigh, King Abdulaziz International Airport and the city of Jeddah in under 3 hours.


title: Medina

text: Though the city's sacred core of the old city is off limits to non-Muslims, the Haram area of Medina itself is much smaller than that of Mecca and Medina has recently seen an increase in the number of Muslim and Non-Muslim expatriate workers of other nationalities, most commonly South Asian peoples and people from other countries in the Gulf Cooperation Council. Almost all of the historic city has been demolished in the Saudi era. The rebuilt city is centered on the vastly expanded "al-Masjid an-Nabawi".


title: Medina

text: Saudi Arabia is hostile to any reverence given to historical or religious places of significance for fear that it may give rise to "shirk" (idolatry). As a consequence, under Saudi rule, Medina has suffered from considerable destruction of its physical heritage including the loss of many buildings over a thousand years old. Critics have described this as "Saudi vandalism" and claim that 300 historic sites linked to Muhammad, his family or companions have been lost in Medina and Mecca over the last 50 years. The most famous example of this is the demolition of al-Baqi.


title: Medina

text: Medina is located in the Hejaz region which is a 200 km (124 mi) wide strip between the Nafud desert and the Red Sea. Located approximately 720 km (447 mi) northwest of Riyadh which is at the center of the Saudi desert, the city is 250 km (155 mi) away from the west coast of Saudi Arabia and at an elevation of approximately above sea level. It lies at 39º36' longitude east and 24º28' latitude north. It covers an area of about . The city has been divided into twelve districts, 7 of which have been categorized as urban districts, while the other 5 have been categorized as suburban.


title: Medina

text: Like most cities in the Hejaz region, Medina is situated at a very high elevation. Almost three times as high as Mecca, the city is situated at above sea level. Mount Uhud is the highest peak in Medina and is 1,077 meters (3,533 feet) tall.


title: Medina

text: Medina is a desert oasis surrounded by the Hejaz Mountains and volcanic hills. The soil surrounding Medina consists of mostly basalt, while the hills, especially noticeable to the south of the city, are volcanic ash which dates to the first geological period of the Paleozoic Era. It is surrounded by a number of famous mountains, most notably "Jabal Al-Hujjaj" (The Pilgrims' Mountain) to the west, Sal'aa Mountain to the north-west, "Jabal al-'Ir" or Caravan Mountain to the south and Mount Uhud to the north. The city is situated on a flat mountain plateau at the tripoint of the three valleys (wadis) of "Wadi al 'Aql", "Wadi al 'Aqiq", and "Wadi al Himdh", for this reason, there are large green areas amidst a dry deserted mountainous region.


title: Medina

text: Under the Köppen climate classification, Medina falls in a hot desert climate region (BWh). Summers are extremely hot and dry with daytime temperatures averaging about with nights about . Temperatures above are not unusual between June and September. Winters are milder, with temperatures from at night to in the day. There is very little rainfall, which falls almost entirely between November and May. In summer, the wind is north-western, while in the spring and winters, is south-western.


title: Medina

text: Medina's importance as a religious site derives from the presence of two mosques, "Masjid Quba'a" and "al-Masjid an-Nabawi". Both of these mosques were built by Muhammad himself. Islamic scriptures emphasize the sacredness of Medina. Medina is mentioned several times in the Quran, two examples are Surah At-Tawbah. verse 101 and Al-Hashr. verse 8. Medinan suras are typically longer than their Meccan counterparts and they are also larger in number. Muhammad al-Bukhari recorded in Sahih Bukhari that Anas ibn Malik quoted Muhammad as saying: ""Medina is a sanctuary from that place to that. Its trees should not be cut and no heresy should be innovated nor any sin should be committed in it, and whoever innovates in it an heresy or commits sins (bad deeds), then he will incur the curse of God, the angels, and all the people.""


title: Medina

text: According to Islamic tradition, a prayer in The Prophet's Mosque equates to 1,000 prayers in any other mosque except the "Masjid al-Haram" where one prayer equates to 100,000 prayers in any other mosque. The mosque was initially just an open space for prayer with a raised and covered "minbar" (pulpit) built within seven months and was located beside Muhammad's "rawdhah" (residence, although the word literally means garden) to its side along with the houses of his wives. The mosque was expanded several times throughout history, with many of its internal features developed over time to suit contemporary standards.


title: Medina

text: The modern Prophet's Mosque is famed for the Green Dome situated directly above Muhammad's "rawdhah," which currently serves as the burial site for Muhammad, Abu Bakr al-Siddiq and Umar ibn al-Khattab and is used in road signage along with its signature minaret as an icon for Medina itself. The entire piazza of the mosque is shaded from the sun by 250 membrane umbrellas.


title: Medina

text: It is Sunnah to perform prayer at the Quba'a Mosque. According to a "hadith", Sahl ibn Hunayf reported that Muhammad said, ""Whoever purifies himself in his house, then comes to the mosque of Quba' and prays in it, he will have a reward like the Umrah pilgrimage.""


title: Medina

text: and in another narration,""Whoever goes out until he comes to this mosque – meaning the Mosque of Quba' – and prays there, that will be equivalent to 'Umrah.""It has been recorded by al-Bukhari and Muslim that Muhammad used to go to Quba'a every Saturday to offer two rak'ahs of Sunnah prayer. The mosque at Quba'a was built by Muhammad himself upon his arrival to the old city of Medina. Quba'a and the mosque has been referred in the Qur'an indirectly in Surah At-Tawbah, verse 108.


title: Medina

text: "Masjid al-Qiblatayn" is another mosque historically important to Muslims. Muslims believe that Muhammad was commanded to change his direction of prayer (qibla) from praying toward Jerusalem to praying toward the Ka'bah at Mecca, as he was commanded in Surah Al-Baqarah, verses 143 and 144. The mosque is currently being expanded to be able to hold more than 4,000 worshippers.


title: Medina

text: Three of these historic six mosques were combined recently into the larger Masjid al-Fath with an open courtyard. Sunni sources claim that there is no "hadith" or any other evidence to prove that Muhammad may have said something about the virtue of these mosques.


title: Medina

text: Al-Baqi' is a significant cemetery in Medina where several family members of Muhammad, caliphs and scholars are known to have been buried.


title: Medina

text: Concerning the end of civilization in Medina, Abu Hurairah is recorded to have said that Muhammad said:"The people will leave Medina in spite of the best state it will have, and none except the wild birds and the beasts of prey will live in it, and the last persons who will die will be two shepherds from the tribe of Muzaina, who will be driving their sheep towards Medina, but will find nobody in it, and when they reach the valley of Thaniyat-al-Wada'h, they will fall down on their faces dead." (al-Bukhari, Vol. 3, Book 30, Hadith 98)Sufyan ibn Abu Zuhair said Muhammad said:"Yemen will be conquered and some people will migrate (from Medina) and will urge their families, and those who will obey them to migrate (to Yemen) although Medina will be better for them; if they but knew. Sham will also be conquered and some people will migrate (from Medina) and will urge their families and those who will obey them, to migrate (to Sham) although Medina will be better for them; if they but knew. 'Iraq will be conquered and some people will migrate (from Medina) and will urge their families and those who will obey them to migrate (to 'Iraq) although Medina will be better for them; if they but knew." (al-Bukhari, Vol. 3, Book 30, Hadith 99)


title: Medina

text: With regards to Medina's protection from plague and ad-Dajjal, the following "ahadith" were recorded:


title: Medina

text: by Abu Bakra:"The terror caused by "Al-Masih Ad-Dajjal" will not enter Medina and at that time Medina will have seven gates and there will be two angels at each gate guarding them." (al-Bukhari, Vol. 3, Book 30, Hadith 103)by Abu Hurairah:"There are angels guarding the entrances (or roads) of Medina, neither plague nor Ad-Dajjal will be able to enter it." (al-Bukhari, Vol. 3, Book 30, Hadith 104)


title: Medina

text: As of 2018, the recorded population was 2,188,138, with a growth rate of 2.32%. Being a destination of Muslims from around the world, Medina witnesses illegal immigration after performing Hajj or Umrah, despite the strict rules the government has enforced. However, the Central Hajj Commissioner Prince Khalid bin Faisal stated that the numbers of illegal staying visitors dropped by 29% in 2018.


title: Medina

text: As with most cities in Saudi Arabia, Islam is the religion followed by the majority of the population of Medina.


title: Medina

text: Sunnis of different schools (Hanafi, Maliki, Shafi'i and Hanbali) constitute the majority, while there is a significant Shia minority in and around Medina, such as the Nakhawila. Outside the haram, there are significant numbers of Non-Muslim migrant workers and expats.


title: Medina

text: Similar to that of Mecca, Medina exhibits a cross-cultural environment, a city where people of many nationalities and cultures live together and interact with each other on a daily basis. This only helps the King Fahd Complex for the Printing of the Holy Quran. Established in 1985, the biggest publisher of Quran in the world, it employs around 1100 people and publishes 361 different publications in many languages. It is reported that more than 400,000 people from around the world visit the complex every year. Every visitor is gifted a free copy of the Qur'an at the end of a tour of the facility.


title: Medina

text: The Al Madinah Museum has several exhibits concerning the cultural and historical heritage of the city featuring different archeological collections, visual galleries and rare images of the old city. It also includes the Hejaz Railway Museum. The Dar Al Madinah Museum opened in 2011 and it uncovers the history of Medina specializing in the architectural and urban heritage of the city. There is no archeology or architecture from the time of Mohammed, except what remains of a few stone defensive towers The Holy Qur'an Exhibition houses rare manuscripts of the Quran, along with other exhibitions that encircle the Masjid an-Nabawi.


title: Medina

text: The Madinah Arts Center, founded in 2018 and operated by the MMDA's Cultural Wing, focuses on modern and contemporary arts. The center aims to enhance arts and enrich the artistic and cultural movement of society, empowering artists of all groups and ages. As of February 2020, before the implementation of social distancing measures and curfews, it held more than 13 group and solo art galleries, along with weekly workshops and discussions. The center is located in King Fahd Park, close to Quba Mosque on an area of 8,200 square meters (88,264 square feet)


title: Medina

text: In 2018, the MMDA launched Madinah Forum of Arabic Calligraphy, an annual forum to celebrate Arabic calligraphy and renowned Arabic calligraphers. The event includes discussions about Arabic Calligraphy, and a gallery to show the work of 50 Arabic calligraphers from 10 countries. The Dar al-Qalam Center for Arabic Calligraphy is located to the northwest of the Masjid an-Nabawi, just across the Hejaz Railway Museum. In April 2020, it was announced that the center was renamed the Prince Mohammed bin Salman Center for Arabic Calligraphy, and upgraded to an international hub for Arabic Calligraphers, in conjunction with the "Year of Arabic Calligraphy" event organized by the Ministry of Culture during the years 2020 and 2021.


title: Medina

text: Other projects launched by the MMDA Cultural Wing include the Madinah Forum of Live Sculpture held at Quba Square, with 16 sculptors from 11 countries. The forum aimed to celebrate sculpture as it is an ancient art, and to attract young artists to this form of art.


title: Medina

text: Historically, Medina's economy was dependent on the sale of dates and other agricultural activities. As of 1920, 139 varieties of dates were being grown in the area, along with other vegetables. Religious tourism plays a major part in Medina's economy, being the second holiest city in Islam, and holding many historical Islamic locations, it attracts more than 7 million annual visitors who come to perform Hajj during the Hajj season, and Umrah throughout the year.


title: Medina

text: Medina has two industrial areas, the larger one was established in 2003 with a total area of 10,000,000 m, and managed by the Saudi Authority for Industrial Cities and Technology Zones (MODON). It is located 50 km from Prince Mohammed bin Abdulaziz International Airport, and 200 km from Yanbu Commercial Port, and has 236 factories, which produce petroleum products, building materials, food products, and many other products. The Knowledge Economic City (KEC) is a Saudi Arabian joint stock company founded in 2010. It focuses on real estate development and knowledge-based industries. The project is under development and is expected to highly increase the number of jobs in Medina by its completion.


title: Medina

text: The Ministry of Education is the governing body of education in the al-Madinah Province and it operates 724 and 773 public schools for boys and girls respectively throughout the province. Taibah High School is one of the most notable schools in Saudi Arabia. Established in 1942, it was the second-largest school in the country at that time. Saudi ministers and government officials have graduated from this high school.


title: Medina

text: Taibah University is a public university providing higher education for the residents of the province, it has 28 colleges, of which 16 are in Medina. It offers 89 academic programs and has a strength of 69210 students as of 2020. The Islamic University, established in 1961, is the oldest higher education institution in the region, with around 22000 students enrolled. It offers majors in Sharia, Qur'an, Usul ad-Din, Hadith, and the Arabic language. The university offers Bachelor of Arts degrees and also Master's and Doctorate degrees. The admission is open to Muslims based on scholarships programs that provide accommodation and living expenses. In 2012, the university expanded its programs by establishing the College of Science, which offers Engineering and Computer science majors. Al Madinah College of Technology, which is governed by TVTC, offers a variety of degree programs including Electrical engineering, Mechanical engineering, Computer Sciences and Electronic Sciences. Private universities at Medina include University of Prince Muqrin, the Arab Open University, and Al Rayyan Colleges.


title: Medina

text: Medina is served by the Prince Mohammad bin Abdulaziz International Airport located off Highway 340. It handles domestic flights, while it has scheduled international services to regional destinations in the Middle East. It is the fourth-busiest airport in Saudi Arabia, handling 8,144,790 passengers in 2018. The airport project was announced as the world's best by "Engineering News-Record"'s 3rd Annual Global Best Projects Competition held on 10 September 2015. The airport also received the first Leadership in Energy and Environmental Design (LEED) Gold certificate in the MENA region. The airport receives higher numbers of passengers during the Hajj.


title: Medina

text: In 2015, the MMDA announced Darb as-Sunnah (Sunnah Path) Project, which aims to develop and transform the 3 km (2 mi) Quba'a Road connecting the Quba'a Mosque to the al-Masjid an-Nabawi to an avenue, paving the whole road for pedestrians and providing service facilities to the visitors. The project also aims to revive the Sunnah where Muhammed used to walk from his house (al-Masjid an-Nabawi) to Quba'a every Saturday afternoon.


title: Medina

text: The city of Medina lies at the junction of two of the most important Saudi highways, Highway 60 and Highway 15. Highway 15 connects Medina to Mecca in the south and onward and Tabuk and Jordan in the north. Highway 60 connects the city with Yanbu, a port city on the Red Sea in the west and Al Qassim in the east. The city is served by three ring roads: King Faisal Road, a 5 km ring road that surrounds Al-Masjid an-Nabawi and the downtown area, King Abdullah Road, a 27 km road that surrounds most of urban Medina and King Khalid Road is the biggest ring road that surrounds the whole city and some rural areas with 60 km of roads.


title: Medina

text: The bus transport system in Medina was established in 2012 by the MMDA and is operated by SAPTCO. The newly established bus system includes 10 lines connecting different regions of the city to Masjid an-Nabawi and the downtown area, and serves around 20,000 passengers on a daily basis. In 2017, the MMDA launched the Madinah Sightseeing Bus service. Open top buses take passengers on sightseeing trips throughout the day with two lines and 11 destinations, including Masjid an-Nabawi, Quba'a Mosque and Masjid al-Qiblatayn and offers audio tour guidance with 8 different languages. By the end of 2019, the MMDA announced its plan to expand the bus network with 15 BRT lines. The project was set to be done in 2023. In 2015, the MMDA announced a three-line metro project in extension to the public transportation master plan in Medina.


title: Medina

text: The historic Ottoman railways were shut down and the railway stations, including the one in Medina, were converted into museums by the Saudi government. The Haramain High Speed Railway (HHR) came into operation in 2018, linking Medina and Mecca, and passes through three stations: Jeddah, King Abdul Aziz International Airport, and King Abdullah Economic City. It runs along 444 kilometers (276 miles) with a speed of 300 km/h, and has an annual capacity of 60 million passengers.


title: Convolutional neural network

text: In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation-equivariant responses known as feature maps. Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input. They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series.


title: Convolutional neural network

text: CNNs are regularized versions of multilayer perceptrons. Multilayer perceptrons usually mean fully connected networks, that is, each neuron in one layer is connected to all neurons in the next layer. The "full connectivity" of these networks make them prone to overfitting data. Typical ways of regularization, or preventing overfitting, include: penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.) CNNs take a different approach towards regularization: they take advantage of the hierarchical pattern in data and assemble patterns of increasing complexity using smaller and simpler patterns embossed in their filters. Therefore, on a scale of connectivity and complexity, CNNs are on the lower extreme.


title: Convolutional neural network

text: Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. Individual cortical neurons respond to stimuli only in a restricted region of the visual field known as the receptive field. The receptive fields of different neurons partially overlap such that they cover the entire visual field.


title: Convolutional neural network

text: CNNs use relatively little pre-processing compared to other image classification algorithms. This means that the network learns to optimize the filters (or kernels) through automated learning, whereas in traditional algorithms these filters are hand-engineered. This independence from prior knowledge and human intervention in feature extraction is a major advantage.


title: Convolutional neural network

text: Convolutional neural networks are a specialized type of artificial neural networks that use a mathematical operation called convolution in place of general matrix multiplication in at least one of their layers. They are specifically designed to process pixel data and are used in image recognition and processing.


title: Convolutional neural network

text: A convolutional neural network consists of an input layer, hidden layers and an output layer. In any feed-forward neural network, any middle layers are called hidden because their inputs and outputs are masked by the activation function and final convolution. In a convolutional neural network, the hidden layers include layers that perform convolutions. Typically this includes a layer that performs a dot product of the convolution kernel with the layer's input matrix. This product is usually the Frobenius inner product, and its activation function is commonly ReLU. As the convolution kernel slides along the input matrix for the layer, the convolution operation generates a feature map, which in turn contributes to the input of the next layer. This is followed by other layers such as pooling layers, fully connected layers, and normalization layers.


title: Convolutional neural network

text: In a CNN, the input is a tensor with a shape: (number of inputs) × (input height) × (input width) × (input channels). After passing through a convolutional layer, the image becomes abstracted to a feature map, also called an activation map, with shape: (number of inputs) × (feature map height) × (feature map width) × (feature map channels).


title: Convolutional neural network

text: Convolutional layers convolve the input and pass its result to the next layer. This is similar to the response of a neuron in the visual cortex to a specific stimulus. Each convolutional neuron processes data only for its receptive field. Although fully connected feedforward neural networks can be used to learn features and classify data, this architecture is generally impractical for larger inputs such as high-resolution images. It would require a very high number of neurons, even in a shallow architecture, due to the large input size of images, where each pixel is a relevant input feature. For instance, a fully connected layer for a (small) image of size 100 × 100 has 10,000 weights for "each" neuron in the second layer. Instead, convolution reduces the number of free parameters, allowing the network to be deeper. For example, regardless of image size, using a 5 × 5 tiling region, each with the same shared weights, requires only 25 learnable parameters. Using regularized weights over fewer parameters avoids the vanishing gradients and exploding gradients problems seen during backpropagation in traditional neural networks. Furthermore, convolutional neural networks are ideal for data with a grid-like topology (such as images) as spatial relations between separate features are taken into account during convolution and/or pooling.


title: Convolutional neural network

text: Convolutional networks may include local and/or global pooling layers along with traditional convolutional layers. Pooling layers reduce the dimensions of data by combining the outputs of neuron clusters at one layer into a single neuron in the next layer. Local pooling combines small clusters, tiling sizes such as 2 × 2 are commonly used. Global pooling acts on all the neurons of the feature map. There are two common types of pooling in popular use: max and average. "Max pooling" uses the maximum value of each local cluster of neurons in the feature map, while "average pooling" takes the average value.


title: Convolutional neural network

text: Fully connected layers connect every neuron in one layer to every neuron in another layer. It is the same as a traditional multilayer perceptron neural network (MLP). The flattened matrix goes through a fully connected layer to classify the images.


title: Convolutional neural network

text: In neural networks, each neuron receives input from some number of locations in the previous layer. In a convolutional layer, each neuron receives input from only a restricted area of the previous layer called the neuron's "receptive field". Typically the area is a square (e.g. 5 by 5 neurons). Whereas, in a fully connected layer, the receptive field is the "entire previous layer". Thus, in each convolutional layer, each neuron takes input from a larger area in the input than previous layers. This is due to applying the convolution over and over, which takes into account the value of a pixel, as well as its surrounding pixels. When using dilated layers, the number of pixels in the receptive field remains constant, but the field is more sparsely populated as its dimensions grow when combining the effect of several layers.


title: Convolutional neural network

text: Each neuron in a neural network computes an output value by applying a specific function to the input values received from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning consists of iteratively adjusting these biases and weights.


title: Convolutional neural network

text: The vectors of weights and biases are called "filters" and represent particular features of the input (e.g., a particular shape). A distinguishing feature of CNNs is that many neurons can share the same filter. This reduces the memory footprint because a single bias and a single vector of weights are used across all receptive fields that share that filter, as opposed to each receptive field having its own bias and vector weighting.


title: Convolutional neural network

text: Work by Hubel and Wiesel in the 1950s and 1960s showed that cat visual cortices contain neurons that individually respond to small regions of the visual field. Provided the eyes are not moving, the region of visual space within which visual stimuli affect the firing of a single neuron is known as its receptive field. Neighboring cells have similar and overlapping receptive fields. Receptive field size and location varies systematically across the cortex to form a complete map of visual space. The cortex in each hemisphere represents the contralateral visual field.


title: Convolutional neural network

text: Hubel and Wiesel also proposed a cascading model of these two types of cells for use in pattern recognition tasks.


title: Convolutional neural network

text: It was inspired by the above-mentioned work of Hubel and Wiesel. The neocognitron introduced the two basic types of layers in CNNs: convolutional layers, and downsampling layers. A convolutional layer contains units whose receptive fields cover a patch of the previous layer. The weight vector (the set of adaptive parameters) of such a unit is often called a filter. Units can share filters. Downsampling layers contain units whose receptive fields cover patches of previous convolutional layers. Such a unit typically computes the average of the activations of the units in its patch. This downsampling helps to correctly classify objects in visual scenes even when the objects are shifted.


title: Convolutional neural network

text: In a variant of the neocognitron called the cresceptron, instead of using Fukushima's spatial averaging, J. Weng et al. introduced a method called max-pooling where a downsampling unit computes the maximum of the activations of the units in its patch. Max-pooling is often used in modern CNNs.


title: Convolutional neural network

text: Several supervised and unsupervised learning algorithms have been proposed over the decades to train the weights of a neocognitron. Today, however, the CNN architecture is usually trained through backpropagation.


title: Convolutional neural network

text: The neocognitron is the first CNN which requires units located at multiple network positions to have shared weights.


title: Convolutional neural network

text: Convolutional neural networks were presented at the Neural Information Processing Workshop in 1987, automatically analyzing time-varying signals by replacing learned multiplication with convolution in time, and demonstrated for speech recognition.


title: Convolutional neural network

text: The time delay neural network (TDNN) was introduced in 1987 by Alex Waibel et al. and was one of the first convolutional networks, as it achieved shift invariance. It did so by utilizing weight sharing in combination with backpropagation training. Thus, while also using a pyramidal structure as in the neocognitron, it performed a global optimization of the weights instead of a local one.


title: Convolutional neural network

text: TDNNs are convolutional networks that share weights along the temporal dimension. They allow speech signals to be processed time-invariantly. In 1990 Hampshire and Waibel introduced a variant which performs a two dimensional convolution. Since these TDNNs operated on spectrograms, the resulting phoneme recognition system was invariant to both shifts in time and in frequency. This inspired translation invariance in image processing with CNNs. The tiling of neuron outputs can cover timed stages.


title: Convolutional neural network

text: In 1990 Yamaguchi et al. introduced the concept of max pooling, which is a fixed filtering operation that calculates and propagates the maximum value of a given region. They did so by combining TDNNs with max pooling in order to realize a speaker independent isolated word recognition system. In their system they used several TDNNs per word, one for each syllable. The results of each TDNN over the input signal were combined using max pooling and the outputs of the pooling layers were then passed on to networks performing the actual word classification.


title: Convolutional neural network

text: A system to recognize hand-written ZIP Code numbers involved convolutions in which the kernel coefficients had been laboriously hand designed.


title: Convolutional neural network

text: Yann LeCun et al. (1989) used back-propagation to learn the convolution kernel coefficients directly from images of hand-written numbers. Learning was thus fully automatic, performed better than manual coefficient design, and was suited to a broader range of image recognition problems and image types.


title: Convolutional neural network

text: Wei Zhang et al. (1988) used back-propagation to train the convolution kernels of a CNN for alphabets recognition. The model was called Shift-Invariant Artificial Neural Network (SIANN) before the name CNN was coined later in the early 1990s. Wei Zhang et al. also applied the same CNN without the last fully connected layer for medical image object segmentation (1991) and breast cancer detection in mammograms (1994).


title: Convolutional neural network

text: LeNet-5, a pioneering 7-level convolutional network by LeCun et al. in 1998, that classifies digits, was applied by several banks to recognize hand-written numbers on checks () digitized in 32x32 pixel images. The ability to process higher-resolution images requires larger and more layers of convolutional neural networks, so this technique is constrained by the availability of computing resources.


title: Convolutional neural network

text: A shift invariant neural network was proposed by Wei Zhang et al. for image character recognition in 1988. It is a modified Neocognitron by keeping only the convolutional interconnections between the image feature layers and the last fully connected layer. The model was trained with back-propagation. The training algorithm were further improved in 1991 to improve its generalization ability. The model architecture was modified by removing the last fully connected layer and applied for medical image segmentation (1991) and automatic detection of breast cancer in mammograms (1994).


title: Convolutional neural network

text: A different convolution-based design was proposed in 1988 for application to decomposition of one-dimensional electromyography convolved signals via de-convolution. This design was modified in 1989 to other de-convolution-based designs.


title: Convolutional neural network

text: The feed-forward architecture of convolutional neural networks was extended in the neural abstraction pyramid by lateral and feedback connections. The resulting recurrent convolutional network allows for the flexible incorporation of contextual information to iteratively resolve local ambiguities. In contrast to previous models, image-like outputs at the highest resolution were generated, e.g., for semantic segmentation, image reconstruction, and object localization tasks.


title: Convolutional neural network

text: Although CNNs were invented in the 1980s, their breakthrough in the 2000s required fast implementations on graphics processing units (GPUs).


title: Convolutional neural network

text: In 2004, it was shown by K. S. Oh and K. Jung that standard neural networks can be greatly accelerated on GPUs. Their implementation was 20 times faster than an equivalent implementation on CPU. In 2005, another paper also emphasised the value of GPGPU for machine learning.


title: Convolutional neural network

text: The first GPU-implementation of a CNN was described in 2006 by K. Chellapilla et al. Their implementation was 4 times faster than an equivalent implementation on CPU. Subsequent work also used GPUs, initially for other types of neural networks (different from CNNs), especially unsupervised neural networks.


title: Convolutional neural network

text: In 2010, Dan Ciresan et al. at IDSIA showed that even deep standard neural networks with many layers can be quickly trained on GPU by supervised learning through the old method known as backpropagation. Their network outperformed previous machine learning methods on the MNIST handwritten digits benchmark. In 2011, they extended this GPU approach to CNNs, achieving an acceleration factor of 60, with impressive results. In 2011, they used such CNNs on GPU to win an image recognition contest where they achieved superhuman performance for the first time. Between May 15, 2011 and September 30, 2012, their CNNs won no less than four image competitions. In 2012, they also significantly improved on the best performance in the literature for multiple image databases, including the MNIST database, the NORB database, the HWDB1.0 dataset (Chinese characters) and the CIFAR10 dataset (dataset of 60000 32x32 labeled RGB images).


title: Convolutional neural network

text: Subsequently, a similar GPU-based CNN by Alex Krizhevsky et al. won the ImageNet Large Scale Visual Recognition Challenge 2012. A very deep CNN with over 100 layers by Microsoft won the ImageNet 2015 contest.


title: Convolutional neural network

text: Compared to the training of CNNs using GPUs, not much attention was given to the Intel Xeon Phi coprocessor.


title: Convolutional neural network

text: A notable development is a parallelization method for training convolutional neural networks on the Intel Xeon Phi, named Controlled Hogwild with Arbitrary Order of Synchronization (CHAOS).


title: Convolutional neural network

text: In the past, traditional multilayer perceptron (MLP) models were used for image recognition. However, the full connectivity between nodes caused the curse of dimensionality, and was computationally intractable with higher-resolution images. A 1000×1000-pixel image with RGB color channels has 3 million weights per fully-connected neuron, which is too high to feasibly process efficiently at scale.


title: Convolutional neural network

text: For example, in CIFAR-10, images are only of size 32×32×3 (32 wide, 32 high, 3 color channels), so a single fully connected neuron in the first hidden layer of a regular neural network would have 32*32*3 = 3,072 weights. A 200×200 image, however, would lead to neurons that have 200*200*3 = 120,000 weights.


title: Convolutional neural network

text: Also, such network architecture does not take into account the spatial structure of data, treating input pixels which are far apart in the same way as pixels that are close together. This ignores locality of reference in data with a grid-topology (such as images), both computationally and semantically. Thus, full connectivity of neurons is wasteful for purposes such as image recognition that are dominated by spatially local input patterns.


title: Convolutional neural network

text: Convolutional neural networks are variants of multilayer perceptrons, designed to emulate the behavior of a visual cortex. These models mitigate the challenges posed by the MLP architecture by exploiting the strong spatially local correlation present in natural images. As opposed to MLPs, CNNs have the following distinguishing features:


title: Convolutional neural network

text: Together, these properties allow CNNs to achieve better generalization on vision problems. Weight sharing dramatically reduces the number of free parameters learned, thus lowering the memory requirements for running the network and allowing the training of larger, more powerful networks.


title: Convolutional neural network

text: A CNN architecture is formed by a stack of distinct layers that transform the input volume into an output volume (e.g. holding the class scores) through a differentiable function. A few distinct types of layers are commonly used. These are further discussed below.


title: Convolutional neural network

text: The convolutional layer is the core building block of a CNN. The layer's parameters consist of a set of learnable filters (or kernels), which have a small receptive field, but extend through the full depth of the input volume. During the forward pass, each filter is convolved across the width and height of the input volume, computing the dot product between the filter entries and the input, producing a 2-dimensional activation map of that filter. As a result, the network learns filters that activate when it detects some specific type of feature at some spatial position in the input.


title: Convolutional neural network

text: Stacking the activation maps for all filters along the depth dimension forms the full output volume of the convolution layer. Every entry in the output volume can thus also be interpreted as an output of a neuron that looks at a small region in the input and shares parameters with neurons in the same activation map.


title: Convolutional neural network

text: When dealing with high-dimensional inputs such as images, it is impractical to connect neurons to all neurons in the previous volume because such a network architecture does not take the spatial structure of the data into account. Convolutional networks exploit spatially local correlation by enforcing a sparse local connectivity pattern between neurons of adjacent layers: each neuron is connected to only a small region of the input volume.


title: Convolutional neural network

text: The extent of this connectivity is a hyperparameter called the receptive field of the neuron. The connections are local in space (along width and height), but always extend along the entire depth of the input volume. Such an architecture ensures that the learnt filters produce the strongest response to a spatially local input pattern.


title: Convolutional neural network

text: Three hyperparameters control the size of the output volume of the convolutional layer: the depth, stride, and padding size:


title: Convolutional neural network

text: The spatial size of the output volume is a function of the input volume size formula_3, the kernel field size formula_4 of the convolutional layer neurons, the stride formula_5, and the amount of zero padding formula_6 on the border. The number of neurons that "fit" in a given volume is then:


title: Convolutional neural network

text: If this number is not an integer, then the strides are incorrect and the neurons cannot be tiled to fit across the input volume in a symmetric way. In general, setting zero padding to be formula_8 when the stride is formula_9 ensures that the input volume and output volume will have the same size spatially. However, it is not always completely necessary to use all of the neurons of the previous layer. For example, a neural network designer may decide to use just a portion of padding.


title: Convolutional neural network

text: A parameter sharing scheme is used in convolutional layers to control the number of free parameters. It relies on the assumption that if a patch feature is useful to compute at some spatial position, then it should also be useful to compute at other positions. Denoting a single 2-dimensional slice of depth as a "depth slice", the neurons in each depth slice are constrained to use the same weights and bias.


title: Convolutional neural network

text: Since all neurons in a single depth slice share the same parameters, the forward pass in each depth slice of the convolutional layer can be computed as a convolution of the neuron's weights with the input volume. Therefore, it is common to refer to the sets of weights as a filter (or a kernel), which is convolved with the input. The result of this convolution is an activation map, and the set of activation maps for each different filter are stacked together along the depth dimension to produce the output volume. Parameter sharing contributes to the translation invariance of the CNN architecture.


title: Convolutional neural network

text: Sometimes, the parameter sharing assumption may not make sense. This is especially the case when the input images to a CNN have some specific centered structure; for which we expect completely different features to be learned on different spatial locations. One practical example is when the inputs are faces that have been centered in the image: we might expect different eye-specific or hair-specific features to be learned in different parts of the image. In that case it is common to relax the parameter sharing scheme, and instead simply call the layer a "locally connected layer".


title: Convolutional neural network

text: Another important concept of CNNs is pooling, which is a form of non-linear down-sampling. There are several non-linear functions to implement pooling, where "max pooling" is the most common. It partitions the input image into a set of rectangles and, for each such sub-region, outputs the maximum.


title: Convolutional neural network

text: Intuitively, the exact location of a feature is less important than its rough location relative to other features. This is the idea behind the use of pooling in convolutional neural networks. The pooling layer serves to progressively reduce the spatial size of the representation, to reduce the number of parameters, memory footprint and amount of computation in the network, and hence to also control overfitting. This is known as down-sampling. It is common to periodically insert a pooling layer between successive convolutional layers (each one typically followed by an activation function, such as a ReLU layer) in a CNN architecture. While pooling layers contribute to local translation invariance, they do not provide global translation invariance in a CNN, unless a form of global pooling is used. The pooling layer commonly operates independently on every depth, or slice, of the input and resizes it spatially. A very common form of max pooling is a layer with filters of size 2×2, applied with a stride of 2, which subsamples every depth slice in the input by 2 along both width and height, discarding 75% of the activations:formula_10


title: Convolutional neural network

text: In this case, every max operation is over 4 numbers. The depth dimension remains unchanged (this is true for other forms of pooling as well).


title: Convolutional neural network

text: In addition to max pooling, pooling units can use other functions, such as average pooling or ℓ-norm pooling. Average pooling was often used historically but has recently fallen out of favor compared to max pooling, which generally performs better in practice.


title: Convolutional neural network

text: Due to the effects of fast spatial reduction of the size of the representation, there is a recent trend towards using smaller filters or discarding pooling layers altogether.


title: Convolutional neural network

text: "Region of Interest" pooling (also known as RoI pooling) is a variant of max pooling, in which output size is fixed and input rectangle is a parameter.


title: Convolutional neural network

text: Pooling is a downsampling method and an important component of convolutional neural networks for object detection based on the Fast R-CNN architecture.


title: Convolutional neural network

text: A CMP operation layer conducts the MP operation along the channel side among the corresponding positions of the consecutive feature maps for the purpose of redundant information elimination. The CMP makes the significant features gather together within fewer channels, which is important for fine-grained image classification that needs more discriminating features. Meanwhile, another advantage of the CMP operation is to make the channel number of feature maps smaller before it connects to the first fully connected (FC) layer. Similar to the MP operation, we denote the input feature maps and output feature maps of a CMP layer as F ∈ R(C×M×N) and C ∈ R(c×M×N), respectively, where C and c are the channel numbers of the input and output feature maps, M and N are the widths and the height of the feature maps, respectively. Note that the CMP operation only changes the channel number of the feature maps. The width and the height of the feature maps are not changed, which is different from the MP operation.


title: Convolutional neural network

text: ReLU is the abbreviation of rectified linear unit, which applies the non-saturating activation function formula_11. It effectively removes negative values from an activation map by setting them to zero. It introduces nonlinearities to the decision function and in the overall network without affecting the receptive fields of the convolution layers.


title: Convolutional neural network

text: Other functions can also be used to increase nonlinearity, for example the saturating hyperbolic tangent formula_12, formula_13, and the sigmoid function formula_14. ReLU is often preferred to other functions because it trains the neural network several times faster without a significant penalty to generalization accuracy.


title: Convolutional neural network

text: After several convolutional and max pooling layers, the final classification is done via fully connected layers. Neurons in a fully connected layer have connections to all activations in the previous layer, as seen in regular (non-convolutional) artificial neural networks. Their activations can thus be computed as an affine transformation, with matrix multiplication followed by a bias offset (vector addition of a learned or fixed bias term).


title: Convolutional neural network

text: The "loss layer", or "loss function", specifies how training penalizes the deviation between the predicted output of the network, and the true data labels (during supervised learning). Various loss functions can be used, depending on the specific task.


title: Convolutional neural network

text: The Softmax loss function is used for predicting a single class of "K" mutually exclusive classes. Sigmoid cross-entropy loss is used for predicting "K" independent probability values in formula_15. Euclidean loss is used for regressing to real-valued labels formula_16.


title: Convolutional neural network

text: Hyperparameters are various settings that are used to control the learning process. CNNs use more hyperparameters than a standard multilayer perceptron (MLP).


title: Convolutional neural network

text: The kernel is the number of pixels processed together. It is typically expressed as the kernel's dimensions, e.g., 2x2, or 3x3.


title: Convolutional neural network

text: Padding is the addition of (typically) 0-valued pixels on the borders of an image. This is done so that the border pixels are not undervalued (lost) from the output because they would ordinarily participate in only a single receptive field instance. The padding applied is typically one less than the corresponding kernel dimension. For example, a convolutional layer using 3x3 kernels would receive a 2-pixel pad, that is 1 pixel on each side of the image.


title: Convolutional neural network

text: The stride is the number of pixels that the analysis window moves on each iteration. A stride of 2 means that each kernel is offset by 2 pixels from its predecessor.


title: Convolutional neural network

text: Since feature map size decreases with depth, layers near the input layer tend to have fewer filters while higher layers can have more. To equalize computation at each layer, the product of feature values "v" with pixel position is kept roughly constant across layers. Preserving more information about the input would require keeping the total number of activations (number of feature maps times number of pixel positions) non-decreasing from one layer to the next.


title: Convolutional neural network

text: The number of feature maps directly controls the capacity and depends on the number of available examples and task complexity.


title: Convolutional neural network

text: Common filter sizes found in the literature vary greatly, and are usually chosen based on the data set.


title: Convolutional neural network

text: The challenge is to find the right level of granularity so as to create abstractions at the proper scale, given a particular data set, and without overfitting.


title: Convolutional neural network

text: Max pooling is typically used, often with a 2x2 dimension. This implies that the input is drastically downsampled, reducing processing cost.


title: Convolutional neural network

text: Large input volumes may warrant 4×4 pooling in the lower layers. Greater pooling reduces the dimension of the signal, and may result in unacceptable information loss. Often, non-overlapping pooling windows perform best.


title: Convolutional neural network

text: Dilation involves ignoring pixels within a kernel. This reduces processing/memory potentially without significant signal loss. A dilation of 2 on a 3x3 kernel expands the kernel to 5x5, while still processing 9 (evenly spaced) pixels. Accordingly, dilation of 4 expands the kernel to 9x9.


title: Convolutional neural network

text: It is commonly assumed that CNNs are invariant to shifts of the input. Convolution or pooling layers within a CNN that do not have a stride greater than one are indeed equivariant to translations of the input. However, layers with a stride greater than one ignore the Nyquist-Shannon sampling theorem and might lead to aliasing of the input signal While, in principle, CNNs are capable of implementing anti-aliasing filters, it has been observed that this does not happen in practice and yield models that are not equivariant to translations.


title: Convolutional neural network

text: Furthermore, if a CNN makes use of fully connected layers, translation equivariance does not imply translation invariance, as the fully connected layers are not invariant to shifts of the input. One solution for complete translation invariance is avoiding any down-sampling throughout the network and applying global average pooling at the last layer. Additionally, several other partial solutions have been proposed, such as anti-aliasing before downsampling operations, spatial transformer networks, data augmentation, subsampling combined with pooling, and capsule neural networks.


title: Convolutional neural network

text: The accuracy of the final model is based on a sub-part of the dataset set apart at the start, often called a test-set. Other times methods such as "k"-fold cross-validation are applied. Other strategies include using conformal prediction.


title: Convolutional neural network

text: Regularization is a process of introducing additional information to solve an ill-posed problem or to prevent overfitting. CNNs use various types of regularization.


title: Convolutional neural network

text: Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net (ignored) with probability formula_17 or kept with probability formula_18, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed. Only the reduced network is trained on the data in that stage. The removed nodes are then reinserted into the network with their original weights.


title: Convolutional neural network

text: In the training stages, formula_18 is usually 0.5; for input nodes, it is typically much higher because information is directly lost when input nodes are ignored.


title: Convolutional neural network

text: At testing time after training has finished, we would ideally like to find a sample average of all possible formula_20 dropped-out networks; unfortunately this is unfeasible for large values of formula_21. However, we can find an approximation by using the full network with each node's output weighted by a factor of formula_18, so the expected value of the output of any node is the same as in the training stages. This is the biggest contribution of the dropout method: although it effectively generates formula_20 neural nets, and as such allows for model combination, at test time only a single network needs to be tested.


title: Convolutional neural network

text: By avoiding training all nodes on all training data, dropout decreases overfitting. The method also significantly improves training speed. This makes the model combination practical, even for deep neural networks. The technique seems to reduce node interactions, leading them to learn more robust features that better generalize to new data.


title: Convolutional neural network

text: DropConnect is the generalization of dropout in which each connection, rather than each output unit, can be dropped with probability formula_17. Each unit thus receives input from a random subset of units in the previous layer.


title: Convolutional neural network

text: DropConnect is similar to dropout as it introduces dynamic sparsity within the model, but differs in that the sparsity is on the weights, rather than the output vectors of a layer. In other words, the fully connected layer with DropConnect becomes a sparsely connected layer in which the connections are chosen at random during the training stage.


title: Convolutional neural network

text: A major drawback to Dropout is that it does not have the same benefits for convolutional layers, where the neurons are not fully connected.


title: Convolutional neural network

text: In stochastic pooling, the conventional deterministic pooling operations are replaced with a stochastic procedure, where the activation within each pooling region is picked randomly according to a multinomial distribution, given by the activities within the pooling region. This approach is free of hyperparameters and can be combined with other regularization approaches, such as dropout and data augmentation.


title: Convolutional neural network

text: An alternate view of stochastic pooling is that it is equivalent to standard max pooling but with many copies of an input image, each having small local deformations. This is similar to explicit elastic deformations of the input images, which delivers excellent performance on the MNIST data set. Using stochastic pooling in a multilayer model gives an exponential number of deformations since the selections in higher layers are independent of those below.


title: Convolutional neural network

text: Because the degree of model overfitting is determined by both its power and the amount of training it receives, providing a convolutional network with more training examples can reduce overfitting. Because these networks are usually trained with all available data, one approach is to either generate new data from scratch (if possible) or perturb existing data to create new ones. For example, input images can be cropped, rotated, or rescaled to create new examples with the same labels as the original training set.


title: Convolutional neural network

text: One of the simplest methods to prevent overfitting of a network is to simply stop the training before overfitting has had a chance to occur. It comes with the disadvantage that the learning process is halted.


title: Convolutional neural network

text: Another simple way to prevent overfitting is to limit the number of parameters, typically by limiting the number of hidden units in each layer or limiting network depth. For convolutional networks, the filter size also affects the number of parameters. Limiting the number of parameters restricts the predictive power of the network directly, reducing the complexity of the function that it can perform on the data, and thus limits the amount of overfitting. This is equivalent to a "zero norm".


title: Convolutional neural network

text: A simple form of added regularizer is weight decay, which simply adds an additional error, proportional to the sum of weights (L1 norm) or squared magnitude (L2 norm) of the weight vector, to the error at each node. The level of acceptable model complexity can be reduced by increasing the proportionality constant('alpha' hyperparameter), thus increasing the penalty for large weight vectors.


title: Convolutional neural network

text: L2 regularization is the most common form of regularization. It can be implemented by penalizing the squared magnitude of all parameters directly in the objective. The L2 regularization has the intuitive interpretation of heavily penalizing peaky weight vectors and preferring diffuse weight vectors. Due to multiplicative interactions between weights and inputs this has the useful property of encouraging the network to use all of its inputs a little rather than some of its inputs a lot.


title: Convolutional neural network

text: L1 regularization is also common. It makes the weight vectors sparse during optimization. In other words, neurons with L1 regularization end up using only a sparse subset of their most important inputs and become nearly invariant to the noisy inputs. L1 with L2 regularization can be combined; this is called elastic net regularization.


title: Convolutional neural network

text: Another form of regularization is to enforce an absolute upper bound on the magnitude of the weight vector for every neuron and use projected gradient descent to enforce the constraint. In practice, this corresponds to performing the parameter update as normal, and then enforcing the constraint by clamping the weight vector formula_25 of every neuron to satisfy formula_26. Typical values of formula_27 are order of 3–4. Some papers report improvements when using this form of regularization.


title: Convolutional neural network

text: Pooling loses the precise spatial relationships between high-level parts (such as nose and mouth in a face image). These relationships are needed for identity recognition. Overlapping the pools so that each feature occurs in multiple pools, helps retain the information. Translation alone cannot extrapolate the understanding of geometric relationships to a radically new viewpoint, such as a different orientation or scale. On the other hand, people are very good at extrapolating; after seeing a new shape once they can recognize it from a different viewpoint.


title: Convolutional neural network

text: An earlier common way to deal with this problem is to train the network on transformed data in different orientations, scales, lighting, etc. so that the network can cope with these variations. This is computationally intensive for large data-sets. The alternative is to use a hierarchy of coordinate frames and use a group of neurons to represent a conjunction of the shape of the feature and its pose relative to the retina. The pose relative to the retina is the relationship between the coordinate frame of the retina and the intrinsic features' coordinate frame.


title: Convolutional neural network

text: Thus, one way to represent something is to embed the coordinate frame within it. This allows large features to be recognized by using the consistency of the poses of their parts (e.g. nose and mouth poses make a consistent prediction of the pose of the whole face). This approach ensures that the higher-level entity (e.g. face) is present when the lower-level (e.g. nose and mouth) agree on its prediction of the pose. The vectors of neuronal activity that represent pose ("pose vectors") allow spatial transformations modeled as linear operations that make it easier for the network to learn the hierarchy of visual entities and generalize across viewpoints. This is similar to the way the human visual system imposes coordinate frames in order to represent shapes.


title: Convolutional neural network

text: CNNs are often used in image recognition systems. In 2012 an error rate of 0.23% on the MNIST database was reported. Another paper on using CNN for image classification reported that the learning process was "surprisingly fast"; in the same paper, the best published results as of 2011 were achieved in the MNIST database and the NORB database. Subsequently, a similar CNN called


title: Convolutional neural network

text: When applied to facial recognition, CNNs achieved a large decrease in error rate. Another paper reported a 97.6% recognition rate on "5,600 still images of more than 10 subjects". CNNs were used to assess video quality in an objective way after manual training; the resulting system had a very low root mean square error.


title: Convolutional neural network

text: The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object classification and detection, with millions of images and hundreds of object classes. In the ILSVRC 2014, a large-scale visual recognition challenge, almost every highly ranked team used CNN as their basic framework. The winner GoogLeNet (the foundation of DeepDream) increased the mean average precision of object detection to 0.439329, and reduced classification error to 0.06656, the best result to date. Its network applied more than 30 layers. That performance of convolutional neural networks on the ImageNet tests was close to that of humans. The best algorithms still struggle with objects that are small or thin, such as a small ant on a stem of a flower or a person holding a quill in their hand. They also have trouble with images that have been distorted with filters, an increasingly common phenomenon with modern digital cameras. By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained categories such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this.


title: Convolutional neural network

text: In 2015 a many-layered CNN demonstrated the ability to spot faces from a wide range of angles, including upside down, even when partially occluded, with competitive performance. The network was trained on a database of 200,000 images that included faces at various angles and orientations and a further 20 million images without faces. They used batches of 128 images over 50,000 iterations.


title: Convolutional neural network

text: Compared to image data domains, there is relatively little work on applying CNNs to video classification. Video is more complex than images since it has another (temporal) dimension. However, some extensions of CNNs into the video domain have been explored. One approach is to treat space and time as equivalent dimensions of the input and perform convolutions in both time and space. Another way is to fuse the features of two convolutional neural networks, one for the spatial and one for the temporal stream. Long short-term memory (LSTM) recurrent units are typically incorporated after the CNN to account for inter-frame or inter-clip dependencies. Unsupervised learning schemes for training spatio-temporal features have been introduced, based on Convolutional Gated Restricted Boltzmann Machines and Independent Subspace Analysis. It's Application can be seen in Text-to-Video model.


title: Convolutional neural network

text: CNNs have also been explored for natural language processing. CNN models are effective for various NLP problems and achieved excellent results in semantic parsing, search query retrieval, sentence modeling, classification, prediction and other traditional NLP tasks.


title: Convolutional neural network

text: Compared to traditional language processing methods such as recurrent neural networks, CNNs can represent different contextual realities of language that do not rely on a series-sequence assumption, while RNNs are better suitable when classical time series modeling is required.


title: Convolutional neural network

text: A CNN with 1-D convolutions was used on time series in the frequency domain (spectral residual) by an unsupervised model to detect anomalies in the time domain.


title: Convolutional neural network

text: CNNs have been used in drug discovery. Predicting the interaction between molecules and biological proteins can identify potential treatments. In 2015, Atomwise introduced AtomNet, the first deep learning neural network for structure-based drug design. The system trains directly on 3-dimensional representations of chemical interactions. Similar to how image recognition networks learn to compose smaller, spatially proximate features into larger, complex structures, AtomNet discovers chemical features, such as aromaticity, sp carbons, and hydrogen bonding. Subsequently, AtomNet was used to predict novel candidate biomolecules for multiple disease targets, most notably treatments for the Ebola virus and multiple sclerosis.


title: Convolutional neural network

text: CNNs can be naturally tailored to analyze a sufficiently large collection of time series data representing one-week-long human physical activity streams augmented by the rich clinical data (including the death register, as provided by, e.g., the NHANES study). A simple CNN was combined with Cox-Gompertz proportional hazards model and used to produce a proof-of-concept example of digital biomarkers of aging in the form of all-causes-mortality predictor.


title: Convolutional neural network

text: CNNs have been used in the game of checkers. From 1999 to 2001, Fogel and Chellapilla published papers showing how a convolutional neural network could learn to play checker using co-evolution. The learning process did not use prior human professional games, but rather focused on a minimal set of information contained in the checkerboard: the location and type of pieces, and the difference in number of pieces between the two sides. Ultimately, the program (Blondie24) was tested on 165 games against players and ranked in the highest 0.4%. It also earned a win against the program Chinook at its "expert" level of play.


title: Convolutional neural network

text: CNNs have been used in computer Go. In December 2014, Clark and Storkey published a paper showing that a CNN trained by supervised learning from a database of human professional games could outperform GNU Go and win some games against Monte Carlo tree search Fuego 1.1 in a fraction of the time it took Fuego to play. Later it was announced that a large 12-layer convolutional neural network had correctly predicted the professional move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional search program GNU Go in 97% of games, and matched the performance of the Monte Carlo tree search program Fuego simulating ten thousand playouts (about a million positions) per move.


title: Convolutional neural network

text: A couple of CNNs for choosing moves to try ("policy network") and evaluating positions ("value network") driving MCTS were used by AlphaGo, the first to beat the best human player at the time.


title: Convolutional neural network

text: Recurrent neural networks are generally considered the best neural network architectures for time series forecasting (and sequence modeling in general), but recent studies show that convolutional networks can perform comparably or even better. Dilated convolutions might enable one-dimensional convolutional neural networks to effectively learn time series dependences. Convolutions can be implemented more efficiently than RNN-based solutions, and they do not suffer from vanishing (or exploding) gradients. Convolutional networks can provide an improved forecasting performance when there are multiple similar time series to learn from. CNNs can also be applied to further tasks in time series analysis (e.g., time series classification or quantile forecasting).


title: Convolutional neural network

text: As archaeological findings like clay tablets with cuneiform writing are increasingly acquired using 3D scanners first benchmark datasets are becoming available like "HeiCuBeDa" providing almost 2.000 normalized 2D- and 3D-datasets prepared with the GigaMesh Software Framework. So curvature-based measures are used in conjunction with Geometric Neural Networks (GNNs) e.g. for period classification of those clay tablets being among the oldest documents of human history.


title: Convolutional neural network

text: For many applications, the training data is less available. Convolutional neural networks usually require a large amount of training data in order to avoid overfitting. A common technique is to train the network on a larger data set from a related domain. Once the network parameters have converged an additional training step is performed using the in-domain data to fine-tune the network weights, this is known as transfer learning. Furthermore, this technique allows convolutional network architectures to successfully be applied to problems with tiny training sets.


title: Convolutional neural network

text: End-to-end training and prediction are common practice in computer vision. However, human interpretable explanations are required for critical systems such as a self-driving cars. With recent advances in visual salience, spatial attention, and temporal attention, the most critical spatial regions/temporal instants could be visualized to justify the CNN predictions.


title: Convolutional neural network

text: A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents, DQNs that utilize CNNs can learn directly from high-dimensional sensory inputs via reinforcement learning.


title: Convolutional neural network

text: Preliminary results were presented in 2014, with an accompanying paper in February 2015. The research described an application to Atari 2600 gaming. Other deep reinforcement learning models preceded it.


title: Convolutional neural network

text: Convolutional deep belief networks (CDBN) have structure very similar to convolutional neural networks and are trained similarly to deep belief networks. Therefore, they exploit the 2D structure of images, like CNNs do, and make use of pre-training like deep belief networks. They provide a generic structure that can be used in many image and signal processing tasks. Benchmark results on standard image datasets like CIFAR have been obtained using CDBNs.


title: Yogurt

text: Yogurt (; , from , also spelled yoghurt, yogourt or yoghourt) is a food produced by bacterial fermentation of milk. The bacteria used to make yogurt are known as "yogurt cultures". Fermentation of sugars in the milk by these bacteria produces lactic acid, which acts on milk protein to give yogurt its texture and characteristic tart flavor. Cow's milk is the milk most commonly used to make yogurt. Milk from water buffalo, goats, ewes, mares, camels, and yaks are also used to produce yogurt. The milk used may be homogenized or not. It may be pasteurized or raw. Each type of milk produces substantially different results.


title: Yogurt

text: Yogurt is produced using a culture of "Lactobacillus delbrueckii" subsp. "bulgaricus" and "Streptococcus thermophilus" bacteria. In addition, other lactobacilli and bifidobacteria are sometimes added during or after culturing yogurt. Some countries require yogurt to contain a specific amount of colony-forming units (CFU) of bacteria; in China, for example, the requirement for the number of lactobacillus bacteria is at least 1 million CFU per milliliter.


title: Yogurt

text: To produce yogurt, milk is first heated, usually to about 85 °C (185 °F), to denature the milk proteins so that they do not form curds. After heating, the milk is allowed to cool to about 45 °C (113 °F). The bacterial culture is mixed in, and a warm temperature of 30–45 °C (86–113 °F) is maintained for 4 to 12 hours to allow fermentation to occur, with the higher temperatures working faster but risking a lumpy texture or whey separation.


title: Yogurt

text: The word is derived from , and is usually related to the verb , "to knead", or "to be curdled or coagulated; to thicken". It may be related to , meaning thick or dense. The sound ğ was traditionally rendered as "gh" in transliterations of Turkish from around 1615–1625.


title: Yogurt

text: In English, spelling variations include "yogurt", "yoghurt", and to a lesser extent "yoghourt" or "yogourt". In the United Kingdom, the word is usually spelled "yoghurt" while in the United States the spelling is "yogurt". In Australia, New Zealand and South Africa, both spellings are commonly found. Canada has its own spelling, "yogourt", a minority variant of the French , although "yogurt" and "yoghurt" are also used.


title: Yogurt

text: Analysis of the "L. delbrueckii" subsp. "bulgaricus" genome indicates that the bacterium may have originated on the surface of a plant. Milk may have become spontaneously and unintentionally exposed to it through contact with plants, or bacteria may have been transferred from the udder of domestic milk-producing animals. The origins of yogurt are unknown, but it was probably invented by Neolithic people in Central Asia and Mesopotamia around 5000 BC, when the first milk-producing animals were domesticated. They most likely found out how to ferment milk by chance and in all likelihood, yogurt was discovered independently in this way in many different places at different times.


title: Yogurt

text: The cuisine of ancient Greece included a dairy product known as oxygala () which was a form of yogurt. Galen (AD 129 – c. 200/c. 216) mentioned that oxygala was consumed with honey, similar to the way thickened Greek yogurt is eaten today. The oldest writings mentioning yogurt are attributed to Pliny the Elder, who remarked that certain "barbarous nations" knew how "to thicken the milk into a substance with an agreeable acidity". The use of yogurt by medieval Turks is recorded in the books "Dīwān Lughāt al-Turk" by Mahmud Kashgari and "Kutadgu Bilig" by Yusuf Has Hajib written in the 11th century. Both texts mention the word "yogurt" in different sections and describe its use by nomadic Turks. The earliest yogurts were probably spontaneously fermented by wild bacteria in goat skin bags.


title: Yogurt

text: Some accounts suggest that Mughal Indian emperor Akbar's cooks would flavor yogurt with mustard seeds and cinnamon. Another early account of a European encounter with yogurt occurs in French clinical history: Francis I suffered from a severe diarrhea which no French doctor could cure. His ally Suleiman the Magnificent sent a doctor, who allegedly cured the patient with yogurt. Being grateful, the French king spread around the information about the food that had cured him.


title: Yogurt

text: Until the 1900s, yogurt was a staple in diets of people in the Russian Empire (and especially Central Asia and the Caucasus), Western Asia, South Eastern Europe/Balkans, Central Europe, and the Indian subcontinent. Stamen Grigorov (1878–1945), a Bulgarian student of medicine in Geneva, first examined the microflora of the Bulgarian yogurt. In 1905, he described it as consisting of a spherical and a rod-like lactic acid-producing bacteria. In 1907, the rod-like bacterium was called "Bacillus bulgaricus" (now "Lactobacillus delbrueckii" subsp. "bulgaricus"). The Russian biologist and Nobel laureate Ilya Mechnikov, from the Institut Pasteur in Paris, was influenced by Grigorov's work and hypothesized that regular consumption of yogurt was responsible for the unusually long lifespans of Bulgarian peasants. Believing "Lactobacillus" to be essential for good health, Mechnikov worked to popularize yogurt as a foodstuff throughout Europe.


title: Yogurt

text: Isaac Carasso industrialized the production of yogurt. In 1919, Carasso, who was from Ottoman Salonika, started a small yogurt business in Barcelona, Spain, and named the business Danone ("little Daniel") after his son. The brand later expanded to the United States under an Americanized version of the name: Dannon. Yogurt with added fruit jam was patented in 1933 by the Radlická Mlékárna dairy in Prague.


title: Yogurt

text: Yogurt was introduced to the United States in the first decade of the twentieth century, influenced by Élie Metchnikoff's "The Prolongation of Life; Optimistic Studies" (1908); it was available in tablet form for those with digestive intolerance and for home culturing. It was popularized by John Harvey Kellogg at the Battle Creek Sanitarium, where it was used both orally and in enemas, and later by Armenian immigrants Sarkis and Rose Colombosian, who started "Colombo and Sons Creamery" in Andover, Massachusetts, in 1929.


title: Yogurt

text: Colombo Yogurt was originally delivered around New England in a horse-drawn wagon inscribed with the Armenian word "madzoon" which was later changed to "yogurt", the Turkish language name of the product, as Turkish was the lingua franca between immigrants of the various Near Eastern ethnicities who were the main consumers at that time. Yogurt's popularity in the United States was enhanced in the 1950s and 1960s, when it was presented as a health food by scientists like Hungarian-born bacteriologist Stephen A. Gaymont. Plain yogurt still proved too sour for the American palate and in 1966 Colombo Yogurt sweetened the yogurt and added fruit preserves, creating "fruit on the bottom" style yogurt. This was successful and company sales soon exceeded $1 million per year. By the late 20th century, yogurt had become a common American food item and Colombo Yogurt was sold in 1993 to General Mills, which discontinued the brand in 2010.


title: Yogurt

text: In 2017, the average American ate of yogurt. The average consumption of yogurt has been declining since 2014.


title: Yogurt

text: Sale of yogurt was down 3.4 percent over the 12 months ending in February 2019. The decline of Greek-style yogurt has allowed Icelandic skyr to gain a foothold in the United States with sales of the latter increasing 24 percent in 2018 to $173 million.


title: Yogurt

text: Yogurt (plain yogurt from whole milk) is 81% water, 9% protein, 5% fat, and 4% carbohydrates, including 4% sugars (table). A 100-gram amount provides of dietary energy. As a proportion of the Daily Value (DV), a serving of yogurt is a rich source of vitamin B (31% DV) and riboflavin (23% DV), with moderate content of protein, phosphorus, and selenium (14 to 19% DV; table).


title: Yogurt

text: The above shows little difference exists between whole milk and yogurt made from whole milk with respect to the listed nutritional constituents.


title: Yogurt

text: Because it may contain live cultures, yogurt is often associated with probiotics, which have been postulated as having positive effects on immune, cardiovascular or metabolic health.


title: Yogurt

text: As of the early 21st century, high-quality clinical evidence was insufficient to conclude that consuming yogurt lowers the risk of diseases or otherwise improves health. Meta-analyses found that consuming 80 grams per day of low-fat yogurt was associated with a lower risk of developing type 2 diabetes and a lower incidence of hip fracture in post-menopausal women. A 2021 review found a cause-and-effect relationship between yogurt consumption and improved lactose tolerance and digestion, and that potential associations exist between yogurt consumption and improving bone health, as well as lowering the risk of some diseases, including cancers and metabolic syndrome.


title: Yogurt

text: Yogurt made with raw milk can be contaminated with bacteria that can cause significant illness and death, including "Listeria", "Cryptosporidium", "Campylobacter", "Brucella", "Escherichia coli" and "Salmonella". Yogurts can also be contaminated with aflatoxin-producing "Aspergillus flavus", "Aspergillus parasiticus" and "Aspergillus nomius".