深度学习基础(影印版)(英文版)

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。

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。

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