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
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