点击选择搜索分类
首页 - 专用软件- 正文
☆☆☆☆☆
||
Josh,Patterson 著
出版社: 东南大学出版社 ISBN:9787564175160 版次:1 商品编码:12319836 包装:平装 外文名称:Deep Learning 开本:16开 出版时间:2018-02-01 用纸:胶版纸
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。
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
深度学习(影印版) [Deep Learning] 电子书 下载 mobi epub pdf txt
深度学习(影印版) [Deep Learning]-so88
深度学习(影印版) [Deep Learning] pdf epub mobi txt 电子书 下载 2022
图书介绍
☆☆☆☆☆
||
Josh,Patterson 著
出版社: 东南大学出版社 ISBN:9787564175160 版次:1 商品编码:12319836 包装:平装 外文名称:Deep Learning 开本:16开 出版时间:2018-02-01 用纸:胶版纸
内容简介
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
作者简介
Josh Patterson目前是Skymind的现场工程副总裁。他此前曾在Cloudera担任高级解决方案架构师,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。
精彩书评
(这本书包含了)开发者所需知道的关于真实世界中深度学习如何起步的一切。
—— Grant Ingersoll (Lucidworks的CTO)
目录
Preface1. 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
深度学习(影印版) [Deep Learning] 电子书 下载 mobi epub pdf txt
电子书下载地址:
相关电子书推荐:
- 文件名
- 大熊猫寻踪——天下成都丛书
- 书.中国制造2025 +机器人 +智慧工厂+ 智能制造+工业大数据【套装5册】书籍图书
- 游戏中玩书画-1
- 农业合作化运动始末:百名亲历者口述实录(插图珍藏本)
- 满58包邮 深圳野生植物识别手册 9787534990298 蒋露 河南科学技术出版社
- 2018增值税政策法规分类汇编解读 2018年新修订版 营改增后增值税政策法规分类汇编解读
- 预售 物质的秘密:藏在微观粒子里的神奇世界 [法] 埃蒂安·克莱恩 著 理想国
- 【赠书签!】京都手艺人+木之匙+日日器物贴+美物抵心+造物有灵且美(精)(共5册)
- 人类神秘现象(智慧点亮人生书系)
- 中国篆刻聚珍(第2辑名家印 第14卷):邓石如
- 关于经典建筑的100个故事 9787305191572
- 商务统计:决策与分析(英文版)(附光盘) [Statistics for Business: Decision Making and Analysis]
- 中华5000年科学故事
- 异形-韦兰-汤谷公司报告
- (青少年“海洋梦”系列丛书)海立云垂——海洋工程与海港 “海洋梦”系列丛书编委会