book

Fundamentals of Deep Learning

Name: Fundamentals of Deep Learning
Author: Nikhil Buduma
ISBN: 9781491925614

by Nikhil Buduma

June 2017

Intermediate to advanced

296 pages

8h 23m

English

O'Reilly Media, Inc.

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Preface
Prerequisites and ObjectivesConventions Used in This BookUsing Code ExamplesSafari® Books OnlineHow to Contact UsAcknowledgements
1. The Neural Network
Building 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 Forward
2. Training Feed-Forward Neural Networks
The 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 NetworksSummary
3. Implementing Neural Networks in TensorFlow
What 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 TensorFlowSummary
4. Beyond Gradient Descent
The 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 SelectionSummary
5. Convolutional Neural Networks
Neurons 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 DomainsSummary
6. Embedding and Representation Learning
Learning 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 ArchitectureSummary
7. Models for Sequence Analysis
Analyzing 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 NetworkSummary
8. Memory Augmented Neural Networks
Neural 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 ComprehendSummary
9. Deep Reinforcement Learning
Deep 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)Summary

Index

Content preview from Fundamentals of Deep Learning

Chapter 3. Implementing Neural Networks in TensorFlow

What Is TensorFlow?

Although we could spend this entire book describing deep learning models in the abstract, we hope that by the end of this text, you not only have an understanding of how deep models work, but also that you are equipped with the skill set required to build these models from scratch for your own problem spaces. Now that we have a better theoretical understanding of deep learning models, we will spend this chapter implementing some of these algorithms in software.

The primary tool that we will use throughout this text is called TensorFlow.¹ TensorFlow is an open source software library released in 2015 by Google to make it easier for developers to design, build, and train deep learning models. TensorFlow originated as an internal library that Google developers used to build models in-house, and we expect additional functionality to be added to the open source version as it is tested and vetted in the internal flavor. Although TensorFlow is only one of several options available to developers, we choose to use it here because of its thoughtful design and ease of use. We’ll briefly compare TensorFlow to alternatives in the next section.

On a high level, TensorFlow is a Python library that allows users to express arbitrary computation as a graph of data flows. Nodes in this graph represent mathematical operations, whereas edges represent data that is communicated from one node to another. Data in TensorFlow is ...

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Fundamentals of Deep Learning

by Nikhil Buduma

Chapter 3. Implementing Neural Networks in TensorFlow

What Is TensorFlow?

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.