作者简介 尼基尔·巴杜马,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,他是靠前生物学奥林匹克竞赛的两枚品牌获得者。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Q Venture 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 Dont 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 DQNs 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
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