<jupyter_start><jupyter_text>Enterprise Deep Learning with TensorFlow: openSAP  SAP Innovation Center Network```Licensed under the Apache License, Version 2.0 (the "License");you may not use this file except in compliance with the License.You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.```  Introduction to Jupyter NotebooksA Jupyter notebook will from hereon be referred to as *notebook*.- Supports multiple programming languages (Julia, Python and R)- Documents with the ability to execute code, and display rich text- Capability to hold human-readable texts, equations, plots, images and tables- Kernel associated with a notebook is automatically launched with notebook  Some Keyboard ShortcutsJupyter notebooks can be run in two modes: - Command mode - Edit mode Following are some keyboard shortcuts commonly used in command mode.Command | Action |------------- |:------------- |Enter | Enter into edit mode [Ctrl / Cmd] + Enter | Run highlighted cell Shift + Enter | Run highlighted cell, select cell below [Alt / Opt] + Enter | Run highlighted cell, insert cell below Y | Change cell to coding mode M | Change cell to markdown mode Up / K | Select cell above current cell Down / J | Select cell below current cell A / B | Insert cell above / below X | Cut selected cell [Ctrl / Cmd] + S | Save and checkpoint notebook  Following are some keyboard shortcuts commonly used in edit mode.Command | Action |------------- |:------------- |Enter | Enter into edit mode [Ctrl / Cmd] + A | Select all text in cell [Ctrl / Cmd] + Z | Undo [Ctrl / Cmd] + Shift + Z | Redo   Running Python Code in JupyterWe will run most of our Python code in the course on a Jupyter notebook. A *notebook* can be used for defining blocks of code. It allows execution and display of intermediate code blocks providing a flexible code development and debugging platform for developers.We see some examples of running Python code below<jupyter_code># Simple example of defining variables and lists
# Initializing values of multiple variables
a, b, c = 15, 20, 10
# Formatting the print output using format command
print("Summing up all the above variables totals to {}".format(a + b + c ))
# The % formatting can also be used to print formatted outputs
print("Summing up all the above variables totals to %d" % (a + b + c))
# Initialize a list of numbers from 0 - 99
num_list = range(100)
# Let's choose only those numbers that are divible by 5 using list comprehension
fiver_list = [num for num in num_list if num % 5 == 0] 
# Creating a formatted string from the list
print("=>".join(str(num) for num in fiver_list))<jupyter_output>0=>5=>10=>15=>20=>25=>30=>35=>40=>45=>50=>55=>60=>65=>70=>75=>80=>85=>90=>95<jupyter_text>Displaying Images in JupyterImages can be displayed in a *notebook* similar to displaying an image on an HTML page.We use the ```![ImageCaption](ImagePath)``` to display images in a *notebook*. The image caption is displayed if the image is not available in the path specified.We display an image below using this method.![SAPLogo](./data/test2.jpg)  Plotting in JupyterOur *notebooks* allow us to plot results and visualize the data that we have.In order to show plots in a notebook, we can use either Matplotlib [].<jupyter_code>import matplotlib.pyplot as plt
%matplotlib inline

plt.plot([1, 2, 3, 4], [10, 20, 30, 40], color='darkblue', linewidth=1, label="Line")
plt.scatter([0.7, 3.8, 1.2, 2.5], [12, 35, 11, 26], color='red', marker='o', label="Scatter") 
plt.title("Plotting using Matplotlib")
plt.legend()
plt.show()<jupyter_output><empty_output><jupyter_text>More plot types and plotting styles are discussed in detail in this article:https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python  Visualizing Machine Learning DatasetsWe will look at an example of using Facets, an open source visualization tool for machine learning datasets.<jupyter_code>from IPython.display import Image

Image(filename='./data/test2.jpg', width=500)

![SAPLogo] (data/test2.jpg)<jupyter_output>/bin/sh: -c: line 0: syntax error near unexpected token `data/test2.jpg'
/bin/sh: -c: line 0: `[SAPLogo] (data/test2.jpg)'
{'fraction_non_alphanumeric': 0.09829825347066726, 'fraction_numerical': 0.021495745633676667, 'mean_word_length': 4.58375, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 31, 'https://': 1, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '51296', 'license': 'Apache-2.0', 'n_tokens_mistral': 1313, 'n_tokens_neox': 1218, 'n_words': 586, 'path': 'Week_01_Unit_03.ipynb', 'repo_name': 'DGCoach/jupyter', 'size': '4466'}