- 深度学习基础(影印版)(英文版)
- - 作者：(美)尼基尔·巴杜马
  - 出版社：东南大学
  - ISBN：9787564175177
  - 出版日期：2018/02/01
  - 页数：283
- 售价：32

内容大纲
随着神经网络在21世纪重振旗鼓，深度学习已成为极其活跃的研究领域，为现代机器学习开辟了道路。在《深度学习基础(影印版)(英文版)》这本实用书籍中，作者尼基尔·巴杜马提供清晰的解释以引导你学完这个复杂领域的主要概念。
Google、微软和Facebook等公司正在积极发展内部的深度学习团队。对于我们而言，尽管如此，深度学习仍然是一门非常复杂和难以掌握的课题。如果你熟悉Python，并且具有微积分背景，以及对于机器学习的基本理解，本书将帮助你开启深度学习之旅。
作者介绍
Nikhil Buduma是Remedy的联合创始人和首席科学家，该公司位于美国旧金山，旨在建立数据驱动为主的健康管理新系统。16岁时，他在圣何塞州立大学管理过一个药物发现实验室，为资源受限的社区研发新颖而低成本的筛查方法。到了19岁，他是国际生物学奥林匹克竞赛的两枚金牌获得者。随后加入MIT，在那里他专注于开发大规模数据系统以影响健康服务、精神健康和医药研究。在MIT，他联合创立了Lean On Me，一家全国性的非营利组织，提供匿名短信热线在大学校园内实现有效的一对一支持，并运用数据来积极影响身心健康。如今，Nikhil通过他的风投基金QVenture Partners投资硬科技和数据公司，还为Milwaukee Brewers篮球队管理一支数据分析团队。
目录
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 Backpropagation Algorithm
  Stochastic and Minibatch Gradient Descent
  Test Sets, Validation Sets, and Overfitting
  Preventing Overfitting in Deep Neural Networks
  Summary
3. Implementing Neural Networks in TensorFIow
  What Is TensorFlow?
  How Does TensorFlow Compare to Alternatives?
  Installing TensorFlow
  Creating and Manipulating TensorFlow Variables
  TensorFlow Operations
  Placeholder Tensors
  Sessions in TensorFlow
  Navigating Variable Scopes and Sharing Variables
  Managing Models over the CPU and GPU
  Specifying the Logistic Regression Model in TensorFlow
  Logging and Training the Logistic Regression Model
  Leveraging TensorBoard to Visualize Computation Graphs and Learning
  Building a Multilayer Model for MNIST in TensorFlow
  Summary
4. Beyond Gradient Descent
  The Challenges with Gradient Descent
  Local Minima in the Error Surfaces of Deep Networks
  Model Identifiability
  How Pesky Are Spurious Local Minima in Deep Networks?
  Flat Regions in the Error Surface
  When the Gradient Points in the 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 in Human Vision
  The Shortcomings of Feature Selection
  Vanilla Deep Neural Networks Don't Scale
  Filters and Feature Maps
  Full Description of the Convolutional Layer
  Max Pooling
  Full Architectural Description of Convolution Networks
  Closing the Loop on MNIST with Convolutional Networks
  Image Preprocessing Pipelines Enable More Robust Models
  Accelerating Training with Batch Normalization
  Building a Convolutional Network for CIFAR-10
  Visualizing Learning in Convolutional Networks
  Leveraging Convolutional Filters to Replicate Artistic Styles
  Learning Convolutional Filters for Other Problem Domains
  Summary
6. Embedding and Representation Learning
  Learning Lower-Dimensional Representations
  Principal Component Analysis
  Motivating the Autoencoder Architecture
  Implementing an Autoencoder in TensorFlow
  Denoising to Force Robust Representations
  Sparsity in Autoencoders
  When Context Is More Informative than the 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 Translation Network
  Summary
8. Memory Augmented Neural Networks
  Neural Turing Machines
  Attention-Based Memory Access
  NTM Memory Addressing Mechanisms
  Differentiable Neural Computers

  Interference-Free Writing in DNCs
  DNC Memory Reuse
  Temporal Linking of DNC Writes
  Understanding the DNC Read Head
  The DNC Controller Network
  Visualizing the DNC in Action
  Implementing the DNC in TensorFlow
  Teaching a DNC to Read and Comprehend
  Summary
9. Deep Reinforcement Learning
  Deep Reinforcement Learning Masters Atari Games
  What Is Reinforcement Learning?
  Markov Decision Processes (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 an Agent
    Building the Model and Optimizer
    Sampling Actions
    Keeping Track of History
    Policy Gradient Main Function
    PGAgent Performance on Pole-Cart
  Q-Learning and Deep Q-Networks
    The Bellman Equation
    Issues with Value Iteration
    Approximating the Q-Function
    Deep Q-Network (DQN)
    Training DQN
    Learning Stability
    Target Q-Network
    Experience Replay
    From Q-Function to Policy
    DQN and the Markov Assumption
    DQN's Solution to 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 Main Loop
    DQNAgent Results on Breakout
  Improving and Moving Beyond DQN
    Deep Recurrent Q-Networks (DRQN)
    Asynchronous Advantage Actor-Critic Agent (A3C)

    UNsupervised REinforcement and Auxiliary Learning (UNREAL)
  Summary
Index

内容大纲

作者介绍

目录

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