深度学习基础

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内容简介

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，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。 * 检验机器学习和神经网络基础 * 学习如何训练前馈神经网络 * 使用TensorFlow实现你的第1个神经网络 * 管理随着网络加深带来的各种问题 * 建立神经网络用于分析复杂图像 * 使用自动编码器实现有效的维度缩减 * 深入了解从序列分析到语言检验 * 掌握强化学习基础

目录

1. 深度学习基础 目录
2. Preface1. The Neural NetworkBuilding Intelligent MachinesThe Limits of Traditional Computer ProgramsThe Mechanics of Machine LearningThe NeuronExpressing Linear Perceptrons as NeuronsFeed-Forward Neural NetworksLinear Neurons and Their LimitationsSigmoid， Tanh， and ReLU NeuronsSoftmax Output LayersLooking Forward2. Training Feed-Forward Neural NetworksThe Fast-Food ProblemGradient DescentThe Delta Rule and Learning RatesGradient Descent with Sigmoidal NeuronsThe Backpropagation AlgorithmStochastic and Minibatch Gradient DescentTest Sets， Validation Sets， and OverfittingPreventing Overfitting in Deep Neural NetworksSummary3. Implementing Neural Networks in TensorFIowWhat Is TensorFlow?How Does TensorFlow Compare to Alternatives?Installing TensorFlowCreating and Manipulating TensorFlow VariablesTensorFlow OperationsPlaceholder TensorsSessions in TensorFlowNavigating Variable Scopes and Sharing VariablesManaging Models over the CPU and GPUSpecifying the Logistic Regression Model in TensorFlowLogging and Training the Logistic Regression ModelLeveraging TensorBoard to Visualize Computation Graphs and LearningBuilding a Multilayer Model for MNIST in TensorFlowSummary4. Beyond Gradient DescentThe Challenges with Gradient DescentLocal Minima in the Error Surfaces of Deep NetworksModel IdentifiabilityHow Pesky Are Spurious Local Minima in Deep Networks?Flat Regions in the Error SurfaceWhen the Gradient Points in the Wrong DirectionMomentum-Based OptimizationA Brief View of Second-Order MethodsLearning Rate AdaptationAdaGrad――Accumulating Historical GradientsRMSProp――Exponentially Weighted Moving Average of GradientsAdam――Combining Momentum and RMSPropThe Philosophy Behind Optimizer SelectionSummary5. Convolutional Neural NetworksNeurons in Human VisionThe Shortcomings of Feature SelectionVanilla Deep Neural Networks Don't ScaleFilters and Feature MapsFull Description of the Convolutional LayerMax PoolingFull Architectural Description of Convolution NetworksClosing the Loop on MNIST with Convolutional NetworksImage Preprocessing Pipelines Enable More Robust ModelsAccelerating Training with Batch NormalizationBuilding a Convolutional Network for CIFAR-10Visualizing Learning in Convolutional NetworksLeveraging Convolutional Filters to Replicate Artistic StylesLearning Convolutional Filters for Other Problem DomainsSummary6. Embedding and Representation LearningLearning Lower-Dimensional RepresentationsPrincipal Component AnalysisMotivating the Autoencoder ArchitectureImplementing an Autoencoder in TensorFlowDenoising to Force Robust RepresentationsSparsity in AutoencodersWhen Context Is More Informative than the Input VectorThe Word2Vec FrameworkImplementing the Skip-Gram ArchitectureSummary7. Models for Sequence AnalysisAnalyzing Variable-Length InputsTackling seq2seq with Neural N-GramsImplementing a Part-of-Speech TaggerDependency Parsing and SyntaxNetBeam Search and Global NormalizationA Case for Stateful Deep Learning ModelsRecurrent Neural NetworksThe Challenges with Vanishing GradientsLong Short-Term Memory （LSTM） UnitsTensorFlow Primitives for RNN ModelsImplementing a Sentiment Analysis ModelSolving seq2seq Tasks with Recurrent Neural NetworksAugmenting Recurrent Networks with AttentionDissecting a Neural Translation NetworkSummary8. Memory Augmented Neural NetworksNeural Turing MachinesAttention-Based Memory AccessNTM Memory Addressing MechanismsDifferentiable Neural ComputersInterference-Free Writing in DNCsDNC Memory ReuseTemporal Linking of DNC WritesUnderstanding the DNC Read HeadThe DNC Controller NetworkVisualizing the DNC in ActionImplementing the DNC in TensorFlowTeaching a DNC to Read and ComprehendSummary9. Deep Reinforcement LearningDeep Reinforcement Learning Masters Atari GamesWhat Is Reinforcement Learning?Markov Decision Processes （MDP）PolicyFuture ReturnDiscounted Future ReturnExplore Versus ExploitPolicy Versus Value LearningPolicy Learning via Policy GradientsPole-Cart with Policy GradientsOpenAI GymCreating an AgentBuilding the Model and OptimizerSampling ActionsKeeping Track of HistoryPolicy Gradient Main FunctionPGAgent Performance on Pole-CartQ-Learning and Deep Q-NetworksThe Bellman EquationIssues with Value IterationApproximating the Q-FunctionDeep Q-Network （DQN）Training DQNLearning StabilityTarget Q-NetworkExperience ReplayFrom Q-Function to PolicyDQN and the Markov AssumptionDQN's Solution to the Markov AssumptionPlaying Breakout wth DQNBuilding Our ArchitectureStacking FramesSetting Up Training OperationsUpdating Our Target Q-NetworkImplementing Experience ReplayDQN Main LoopDQNAgent Results on BreakoutImproving and Moving Beyond DQNDeep Recurrent Q-Networks （DRQN）Asynchronous Advantage Actor-Critic Agent （A3C）UNsupervised REinforcement and Auxiliary Learning （UNREAL）SummaryIndex
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