Introduction - Deep Learning

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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 +. +.

Information Theory 4. Numerical Computation 5. Machine Learning Basics Part II: Deep Networks: Modern Practices 6. Deep Feedforward Networks 7. Regularization 8. Optimization 9. CNNs 10. RNNs 11. Practical Methodology 12. Applications Part III: Deep Learning Research 13. Linear Factor Models 14. Autoencoders 15. Representation Learning 16 ...

  Learning, Theory, Deep, Deep learning