●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
terior 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
ltilayer 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
……
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

在引入開源Deeplearning4j（DL4J）庫用於開發產品級工作流之前，作者Josh Patterson和Adam Gibson介紹了深度學習——調優、並行化、向量化及建立管道——任何庫所需的基礎知識。通過真實的案例，你將學會在Spark和Hadoop上用DL4J訓練深度網絡架構並運行深度學習工作流的方法和策略。
* 深入機器學習一般概念，特別是深度學習相關概念
* 理解深度網絡如何從神經網絡基礎演化
* 探索主流深度網絡架構，包括Convolutional和Recurrent
* 學習如何將特定的深度網絡映射到具體的問題
* 一般神經網絡和特定深度網絡架構調優基礎概覽
* 為不同的數據類型使用DL4J的工作流工具DateVec實現向量化
* 學習如何在Spark和Hadoop本地使用DL4J

(美)喬希·帕特森(Josh Patterson),(美)亞當·吉普森(Adam Gibson) 著 著