The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

Deep learning of representations: looking forward. In Statistical Language and Speech Processing, volume 7978 of Lecture Notes in Computer Science, pages 1–37.

Thisgeneralprincipleofimprovingmodelsbylearningfeaturesextendsbeyond thefeedforwardnetworksdescribedinthischapter. Itisarecurringthemeof …

CHAPTER 1. INTR ODUCTION are built on top of eac h other, the graph is deep, with man y la y ers. F or this reason, w e call this approach to AI deep learning.

Of all the many optimization problems in v olv ed in deep learning, the most difficult

x thathas nondiagonal covariance. Learning inthe mPoT model—again,likethe mcRBM—iscom- plicatedbytheinabilitytosamplefromthenondiagonalGaussianconditional p mPoT

This webpage provides an overview of recurrent neural networks in deep learning.

External Links Commonlounge community for discussing the book Reading group videos for every chapter, from a reading group organized by Alena Kruchkova

Ian's presentation at the 2016 Re-Work Deep Learning Summit. Covers Google Brain research on optimization, including visualization of neural network cost functions, Net2Net, and batch …

“supervisor,”butthetermstillappliesevenwhenthetrainingsettargetswere collectedautomatically. 5.7.1ProbabilisticSupervisedLearning Most supervised learning algorithms inthis book are …