Introduction - Deep Learning

Introduction Lecture slides for Chapter 1 of Deep Learning Ian Goodfellow 2016-09-26. Representations Matter APTER 1. Introduction . Cartesian coordinates Polar coordinates y . x r Figure suppose we want to separate ure : Example of di erent representations: (Goodfellow 2016). Depth: Repeated Composition CHAPTER 1. Introduction . Output CAR PERSON ANIMAL. (object identity). 3rd hidden layer (object parts). 2nd hidden layer (corners and contours). 1st hidden layer (edges). Visible layer (input pixels). Figure : Illustration of a deep Learning model. It is di cult for a computer to understand Figure the meaning of raw sensory input data, such as this image represented as a collection (Goodfellow 2016). Computational Graphs CHAPTER 1. Introduction . Element Element Set Set +. +. Logistic Regression Logistic Regression w1 x1 w2 x2 w x Figure : Illustration of computational graphs mapping an input to an output where Figure each node performs an operation.

human time and e ffort; it can take decades for an entire community of researchers. The quintessential example of a representation learning algorithm is the au-toencoder. An autoencoder is the combination of an encoder function that converts the input data into a different representation, and a decoder function

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