深度學習基礎

《深度學習基礎》是2018年東南大學出版社出版的圖書。

基本介紹

• 中文名：深度學習基礎
• 作者：Nikhil Buduma
• 出版時間：2018年
• 出版社：東南大學出版社
• ISBN：9787564175177
• 類別：人工智慧
• 開本：16 開
• 裝幀：平裝-膠訂

內容簡介

Google、微軟和Facebook等公司正在積極發展內部的深度學習團隊。對於我們而言，深度學習仍然是一門非常複雜和難以掌握的課題。如果你熟悉Python，並且具有微積分背景，以及對於機器學習的基本理解，本書將幫助你開啟深度學習之旅。

* 檢驗機器學習和神經網路基礎

* 學習如何訓練前饋神經網路

* 使用TensorFlow實現你的個神經網路

* 管理隨著網路加深帶來的各種問題

* 建立神經網路用於分析複雜圖像

* 使用自動編碼器實現有效的維度縮減

* 深入了解從序列分析到語言檢驗

* 掌握強化學習基礎

圖書目錄

Preface

1. The Neural Network

Building Intelligent Machines

The Limits of Traditional Computer Programs

The Mechanics of Machine Learning

The Neuron

Expressing Linear Perceptrons as Neurons

Feed-Forward Neural Networks

Linear Neurons and Their Limitations

Sigmoid， Tanh， and ReLU Neurons

Softmax Output Layers

Looking Forward

2. Training Feed-Forward Neural Networks

The Fast-Food Problem

Gradient Descent

The Delta Rule and Learning Rates

Gradient Descent with Sigmoidal Neurons

The BackpropagatioAlgorithm

Stochastic and Minibatch Gradient Descent

Test Sets， ValidatioSets， and Overfitting

Preventing Overfitting iDeep Neural Networks

Summary

3. Implementing Neural Networks iTensorFIow

What Is TensorFlow

How Does TensorFlow Compare to Alternatives

Installing TensorFlow

Creating and Manipulating TensorFlow Variables

TensorFlow Operations

Placeholder Tensors

Sessions iTensorFlow

Navigating Variable Scopes and Sharing Variables

Managing Models over the CPU and GPU

Specifying the Logistic RegressioModel iTensorFlow

Logging and Training the Logistic RegressioModel

Leveraging TensorBoard to Visualize ComputatioGraphs and Learning

Building a Multilayer Model for MNIST iTensorFlow

Summary

4. Beyond Gradient Descent

The Challenges with Gradient Descent

Local Minima ithe Error Surfaces of Deep Networks

Model Identifiability

How Pesky Are Spurious Local Minima iDeep Networks

Flat Regions ithe Error Surface

Whethe Gradient Points ithe Wrong Direction

Momentum-Based Optimization

A Brief View of Second-Order Methods

Learning Rate Adaptation

AdaGrad——Accumulating Historical Gradients

RMSProp——Exponentially Weighted Moving Average of Gradients

Adam——Combining Momentum and RMSProp

The Philosophy Behind Optimizer Selection

Summary

5. Convolutional Neural Networks

Neurons iHumaVision

The Shortings of Feature Selection

Vanilla Deep Neural Networks Don't Scale

Filters and Feature Maps

Full Descriptioof the Convolutional Layer

Max Pooling

Full Architectural Descriptioof ConvolutioNetworks

Closing the Loop oMNIST with Convolutional Networks

Image Preprocessing Pipelines Enable More Robust Models

Accelerating Training with Batch Normalization

Building a Convolutional Network for CIFAR-10

Visualizing Learning iConvolutional Networks

Leveraging Convolutional Filters to Replicate Artistic Styles

Learning Convolutional Filters for Other Problem Domains

Summary

6. Embedding and RepresentatioLearning

Learning Lower-Dimensional Representations

Principal Component Analysis

Motivating the Autoencoder Architecture

Implementing aAutoencoder iTensorFlow

Denoising to Force Robust Representations

Sparsity iAutoencoders

WheContext Is More Informative thathe Input Vector

The Word2Vec Framework

Implementing the Skip-Gram Architecture

Summary

7. Models for Sequence Analysis

Analyzing Variable-Length Inputs

Tackling seq2seq with Neural N-Grams

Implementing a Part-of-Speech Tagger

Dependency Parsing and SyntaxNet

Beam Search and Global Normalization

A Case for Stateful Deep Learning Models

Recurrent Neural Networks

The Challenges with Vanishing Gradients

Long Short-Term Memory （LSTM） Units

TensorFlow Primitives for RNN Models

Implementing a Sentiment Analysis Model

Solving seq2seq Tasks with Recurrent Neural Networks

Augmenting Recurrent Networks with Attention

Dissecting a Neural TranslatioNetwork

Summary

8. Memory Augmented Neural Networks

Neural Turing Machines

Attention-Based Memory Access

NTM Memory Addressing Mechanisms

Differentiable Neural Computers

Interference-Free Writing iDNCs

DNC Memory Reuse

Temporal Linking of DNC Writes

Understanding the DNC Read Head

The DNC Controller Network

Visualizing the DNC iAction

Implementing the DNC iTensorFlow

Teaching a DNC to Read and Comprehend

Summary

9. Deep Reinforcement Learning

Deep Reinforcement Learning Masters Atari Games

What Is Reinforcement Learning

Markov DecisioProcesses （MDP）

Policy

Future Return

Discounted Future Return

Explore Versus Exploit

Policy Versus Value Learning

Policy Learning via Policy Gradients

Pole-Cart with Policy Gradients

OpenAI Gym

Creating aAgent

Building the Model and Optimizer

Sampling Actions

Keeping Track of History

Policy Gradient MaiFunction

PGAgent Performance oPole-Cart

Q-Learning and Deep Q-Networks

The BellmaEquation

Issues with Value Iteration

Approximating the Q-Function

Deep Q-Network （DQN）

Training DQN

Learning Stability

Target Q-Network

Experience Replay

From Q-Functioto Policy

DQN and the Markov Assumption

DQN's Solutioto the Markov Assumption

Playing Breakout wth DQN

Building Our Architecture

Stacking Frames

Setting Up Training Operations

Updating Our Target Q-Network

Implementing Experience Replay

DQN MaiLoop

DQNAgent Results oBreakout

Improving and Moving Beyond DQN

Deep Recurrent Q-Networks （DRQN）

Asynchronous Advantage Actor-Critic Agent （A3C）

UNsupervised REinforcement and Auxiliary Learning （UNREAL）

Summary

Index