Deep Learning
Contents Websiteviii Acknowledgmentsix Notationxiii 1 Introduction1 1.1 WhoShouldReadThisBook?. . . . . . . . . . . . . . . . . . . . 8 1.2 ...
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Introduction - Deep Learning
www.deeplearningbook.orghuman 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
Autoencoders - Deep Learning
www.deeplearningbook.orgbiologically plausible than back-propagation, but is rarely used for machine learning applications. x r h f g Figure 14.1: The general structure of an autoencoder, mapping an input x to an output (called reconstruction) r through an internal representation or code h. The autoencoder
Regularization for Deep Learning
www.deeplearningbook.orgLecture slides for Chapter 7 of Deep Learning www.deeplearningbook.org ... of an abstract, general, quadratic cost function. How do these effects relate to ... 14 1 19 2 23 3 7 7 7 7 5 = 2 6 6 6 6 4 3 1254 1 423 11 3 15 4 23 2 312303 54225 1 3 7 7 7 7 5 2 6 6 6 6 6 6 4 0 2 0 0 3 0 3 7 7 7 7 7 7 5 y 2 Rm B 2 Rm⇥n h 2 Rn (7.47)
Deep Learning
www.deeplearningbook.orgBibliography Abadi,M.,Agarwal,A.,Barham,P.,Brevdo,E.,Chen,Z.,Citro,C.,Corrado,G.S.,Davis, A.,Dean,J.,Devin,M.,Ghemawat,S.,Goodfellow,I.,Harp,A.,Irving,G.,Isard,M.,
Machine Learning Basics
www.deeplearningbook.orgBasics Lecture slides for Chapter 5 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-26 (Goodfellow 2016) Linear Regression CHAPTER 5. MACHINE LEARNING BASICS ... So far we have discussed the properties of various estimators for a training set of >. > ...
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