Preface
1. A Review of Machine Learning
The Learning Machines
How Can Machines Learn?
Biological Inspiration
What Is Deep Learning?
Going Down the Rabbit Hole
Framing the Questions
The Math Behind Machine Learning�� Linear Algebra
Scalars
Vectors
Matrices
Tensors
Hyperplanes
Relevant Mathematical Operations
Converting Data Into Vectors
Solving Systems of Equations
The Math Behind Machine Learning�� Statistics
Probability
Conditional Probabilities
Posterior Probability
Distributions
Samples Versus Population
Resampling Methods
Selection Bias
Likelihood
How Does Machine Learning Work?
Regression
Classification
Clustering
Underfitting and Overfitting
Optimization
Convex Optimization
Gradient Descent
Stochastic Gradient Descent
Quasi-Newton Optimization Methods
Generative Versus Discriminative Models
Logistic Regression
The Logistic Function
Understanding Logistic Regression Output
Evaluating Models
The Confusion Matrix
Building an Understanding of Machine Learning

2. Foundations of Neural Networks and Deep Learning.
Neural Networks
The Biological Neuron
The Perceptron
Multilayer Feed-Forward Networks
Training Neural Networks
Backpropagation Learning
Activation Functions
Linear
Sigmoid
Tanh
Hard Tanh
Softmax
Rectified Linear
Loss Functions
Loss Function Notation
Loss Functions for Regression
Loss Functions for Classification
Loss Functions for Reconstruction
Hyperparameters
Learning Rate
Regularization
Momentum
Sparsity

3. Fundamentals of Deep Networks
4. Major Architectures of Deep Networks
5. Building Deep Networks
6. Tuning Deep Networks
7. Tuning Specific Deep Networks Architecture
8. Vectorization
9. Using Deep Learning and DL4J on Spark
A. What Is Artificial Intelligence?
B. RL4J and Reinforcement Learning
C. Numbers Everyone Should Know
D. Neural Networks and Backpropagation�� A Mathematical Approach
E. Using the ND4J API