Deep Learning
- Cambridge MIT Press 2016
- xiii, 775p.
- Adaptive Computation and Machine Learning series .
978-0262035613
Linear Algebra Probability and Information Theory Numerical Computation Machine Learning Basics Deep Feedforward Networks Regularization for Deep Learning Optimization for Training Deep Models Convolutional Networks Sequence Modeling: Recurrent and Recursive Nets Practical Methodology Linear Factor Models Autoencoders Representation Learning Structured Probabilistic Models for Deep Learning Monte Carlo Methods Confronting the Partition Function Approximate Inference Deep Generative Models