深度学习
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作者(美)乔希·帕特森(Josh Patterson),(美)亚当·吉普森(Adam Gibson) 著
出版社东南大学出版社
ISBN9787564175160
出版时间2018-02
装帧平装
开本16开
定价99元
货号1201662644
上书时间2024-09-30
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作者简介
Josh Patterson目前是Skymind的现场工程副总裁。他此前曾在Cloudera担任不错解决方案架构师，在Tennessee Valley Authority担任机器学习和分布式系统工程师。
Adam Gibson是Skymind的CTO。Adam曾与财富500强企业、对冲基金、公关公司和创投加速器等机构合作，创建它们的机器学习项目。他在帮助这些公司处理和阐释大规模实时数据方面颇具深厚经验。
目录
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
……
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 Artifi Intelligence?
B. RL4J and Reinforcement Learning
C. Numbers Everyone Should Know
D. Neural Networks and Backpropagation： A Mathematical Approach
E. Using the ND4J API
内容摘要
在引入开源Deeplearning4j（DL4J）库用于开发产品级工作流之前，作者Josh Patterson和Adam Gibson介绍了深度学习——调优、并行化、向量化及建立管道——任何库所需的基础知识。通过真实的案例，你将学会在Spark和Hadoop上用DL4J训练深度网络架构并运行深度学习工作流的方法和策略。
* 深入机器学习一般概念，特别是深度学习相关概念
* 理解深度网络如何从神经网络基础演化
* 探索主流深度网络架构，包括Convolutional和Recurrent
* 学习如何将特定的深度网络映射到具体的问题
* 一般神经网络和特定深度网络架构调优基础概览
* 为不同的数据类型使用DL4J的工作流工具DateVec实现向量化
* 学习如何在Spark和Hadoop本地使用DL4J
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