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+"10–19.12adaptedfromhttp://tinyurl.com/4ueyhtsu. this work is subject to a creative commons cc-by-nc-nd license. (c) mit press.chapter 1 introduction artificial intelligence, orai,isconcernedwithbuildingsystemsthatsimulateintelligent behavior. it encompasses a wide range of approaches, including those based on logic, search, and probabilistic reasoning. machine learning is a subset of ai that learns to make decisions by fitting mathematical models to observed data. this area has seen explosive growth and is now (incorrectly) almost synonymous with the term ai. a deep neural network is a type of machine learning model, and when it is fitted to data, this is referred to as deep learning. at the time of writing, deep networks are the most powerful and practical machine learning models and are often encountered in day-to-day life. it is commonplace to translate text from another language using a natural language processing algorithm, to search the internet for images of a particular objectusingacomputervisionsystem,ortoconversewithadigitalassistantviaaspeech recognition interface. all of these applications are powered by deep learning. as the title suggests, this book aims to help a reader new to this field understand the principles behind deep learning. the book is neither terribly theoretical (there are no proofs) nor extremely practical (there is almost no code). the goal is to explain the underlying ideas; after consuming this volume, the reader will be able to apply deep learning to novel situations where there is no existing recipe for success. machinelearningmethodscancoarselybedividedintothreeareas: supervised,unsu- pervised, and reinforcement learning. at the time of writing, the cutting-edge methods in all three areas rely on deep learning (figure 1.1). this introductory chapter describes thesethreeareasatahighlevel,andthistaxonomyisalsolooselyreflectedinthebook’s organization. whether we like it or not, deep learning is poised to change our world, and this change will not all be positive. hence, this chapter also contains brief primer on ai ethics. we conclude with advice on how to make the most of this book. 1.1 supervised learning supervised learning models define a mapping from input data to an output prediction. in the following sections, we discuss the inputs, the outputs, the model itself, and what is meant by “training” a model. draft: please send errata to [email protected] 1 introduction figure 1.1 machine learning is an area of artificial intelligence that fits math- ematical models to observed data. it can coarsely be divided into supervised learning, unsupervised learning, and re- inforcement learning. deep neural net- works contribute to each of these areas. 1.1.1 regression and classification problems figure 1.2 depicts several regression and classification problems. in each case, there is a meaningfulreal-worldinput(asentence,asoundfile,animage,etc.),andthisisencoded asavectorofnumbers. thisvectorformsthemodelinput. themodelmapstheinputto an output vector which is then “translated” back to a meaningful real-world prediction. for now, we focus on the inputs and outputs and treat the model as a black box that ingests a vector of numbers and returns another vector of numbers. the model in figure 1.2a predicts the price of a house based on input characteristics such as the square footage and the number of bedrooms. this is a regression problem becausethemodelreturnsacontinuousnumber(ratherthanacategoryassignment). in contrast, the model in 1.2b takes the chemical structure of a molecule as an input and predicts both the melting and boiling points. this is a multivariate regression problem since it predicts more than one number. themodelinfigure1.2creceivesatextstringcontainingarestaurantreviewasinput and predicts whether the review is positive or negative. this is a binary classification problem because the model attempts to assign the input to one of two categories. the output vector contains the probabilities that the input belongs to each category. fig- ures 1.2d and 1.2e depict multiclass classification problems. here, the model assigns the input to one of n >2 categories. in the first case, the input is an audio file, and the model predicts which genre of music it contains. in the second case, the input is an image, and the model predicts"