book

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

Name: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
Author: Aurélien Géron
ISBN: 9781098125974

by Aurélien Géron

October 2022

Intermediate to advanced

864 pages

25h 26m

English

O'Reilly Media, Inc.

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Preface
The Machine Learning TsunamiMachine Learning in Your ProjectsObjective and ApproachCode ExamplesPrerequisitesRoadmapChanges Between the First and the Second EditionChanges Between the Second and the Third EditionOther ResourcesConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
I. The Fundamentals of Machine Learning
1. The Machine Learning Landscape
What Is Machine Learning?Why Use Machine Learning?Examples of ApplicationsTypes of Machine Learning SystemsTraining SupervisionBatch Versus Online LearningInstance-Based Versus Model-Based LearningMain Challenges of Machine LearningInsufficient Quantity of Training DataNonrepresentative Training DataPoor-Quality DataIrrelevant FeaturesOverfitting the Training DataUnderfitting the Training DataStepping BackTesting and ValidatingHyperparameter Tuning and Model SelectionData MismatchExercises
2. End-to-End Machine Learning Project
Working with Real DataLook at the Big PictureFrame the ProblemSelect a Performance MeasureCheck the AssumptionsGet the DataRunning the Code Examples Using Google ColabSaving Your Code Changes and Your DataThe Power and Danger of InteractivityBook Code Versus Notebook CodeDownload the DataTake a Quick Look at the Data StructureCreate a Test SetExplore and Visualize the Data to Gain InsightsVisualizing Geographical DataLook for CorrelationsExperiment with Attribute CombinationsPrepare the Data for Machine Learning AlgorithmsClean the DataHandling Text and Categorical AttributesFeature Scaling and TransformationCustom TransformersTransformation PipelinesSelect and Train a ModelTrain and Evaluate on the Training SetBetter Evaluation Using Cross-ValidationFine-Tune Your ModelGrid SearchRandomized SearchEnsemble MethodsAnalyzing the Best Models and Their ErrorsEvaluate Your System on the Test SetLaunch, Monitor, and Maintain Your SystemTry It Out!Exercises
3. Classification
MNISTTraining a Binary ClassifierPerformance MeasuresMeasuring Accuracy Using Cross-ValidationConfusion MatricesPrecision and RecallThe Precision/Recall Trade-offThe ROC CurveMulticlass ClassificationError AnalysisMultilabel ClassificationMultioutput ClassificationExercises
4. Training Models
Linear RegressionThe Normal EquationComputational ComplexityGradient DescentBatch Gradient DescentStochastic Gradient DescentMini-Batch Gradient DescentPolynomial RegressionLearning CurvesRegularized Linear ModelsRidge RegressionLasso RegressionElastic Net RegressionEarly StoppingLogistic RegressionEstimating ProbabilitiesTraining and Cost FunctionDecision BoundariesSoftmax RegressionExercises
5. Support Vector Machines
Linear SVM ClassificationSoft Margin ClassificationNonlinear SVM ClassificationPolynomial KernelSimilarity FeaturesGaussian RBF KernelSVM Classes and Computational ComplexitySVM RegressionUnder the Hood of Linear SVM ClassifiersThe Dual ProblemKernelized SVMsExercises
6. Decision Trees
Training and Visualizing a Decision TreeMaking PredictionsEstimating Class ProbabilitiesThe CART Training AlgorithmComputational ComplexityGini Impurity or Entropy?Regularization HyperparametersRegressionSensitivity to Axis OrientationDecision Trees Have a High VarianceExercises
7. Ensemble Learning and Random Forests
Voting ClassifiersBagging and PastingBagging and Pasting in Scikit-LearnOut-of-Bag EvaluationRandom Patches and Random SubspacesRandom ForestsExtra-TreesFeature ImportanceBoostingAdaBoostGradient BoostingHistogram-Based Gradient BoostingStackingExercises
8. Dimensionality Reduction
The Curse of DimensionalityMain Approaches for Dimensionality ReductionProjectionManifold LearningPCAPreserving the VariancePrincipal ComponentsProjecting Down to d DimensionsUsing Scikit-LearnExplained Variance RatioChoosing the Right Number of DimensionsPCA for CompressionRandomized PCAIncremental PCARandom ProjectionLLEOther Dimensionality Reduction TechniquesExercises

9. Unsupervised Learning Techniques
Clustering Algorithms: k-means and DBSCANk-meansLimits of k-meansUsing Clustering for Image SegmentationUsing Clustering for Semi-Supervised LearningDBSCANOther Clustering AlgorithmsGaussian MixturesUsing Gaussian Mixtures for Anomaly DetectionSelecting the Number of ClustersBayesian Gaussian Mixture ModelsOther Algorithms for Anomaly and Novelty DetectionExercises
II. Neural Networks and Deep Learning
10. Introduction to Artificial Neural Networks with Keras
From Biological to Artificial NeuronsBiological NeuronsLogical Computations with NeuronsThe PerceptronThe Multilayer Perceptron and BackpropagationRegression MLPsClassification MLPsImplementing MLPs with KerasBuilding an Image Classifier Using the Sequential APIBuilding a Regression MLP Using the Sequential APIBuilding Complex Models Using the Functional APIUsing the Subclassing API to Build Dynamic ModelsSaving and Restoring a ModelUsing CallbacksUsing TensorBoard for VisualizationFine-Tuning Neural Network HyperparametersNumber of Hidden LayersNumber of Neurons per Hidden LayerLearning Rate, Batch Size, and Other HyperparametersExercises
11. Training Deep Neural Networks
The Vanishing/Exploding Gradients ProblemsGlorot and He InitializationBetter Activation FunctionsBatch NormalizationGradient ClippingReusing Pretrained LayersTransfer Learning with KerasUnsupervised PretrainingPretraining on an Auxiliary TaskFaster OptimizersMomentumNesterov Accelerated GradientAdaGradRMSPropAdamAdaMaxNadamAdamWLearning Rate SchedulingAvoiding Overfitting Through Regularizationℓ1 and ℓ2 RegularizationDropoutMonte Carlo (MC) DropoutMax-Norm RegularizationSummary and Practical GuidelinesExercises
12. Custom Models and Training with TensorFlow
A Quick Tour of TensorFlowUsing TensorFlow like NumPyTensors and OperationsTensors and NumPyType ConversionsVariablesOther Data StructuresCustomizing Models and Training AlgorithmsCustom Loss FunctionsSaving and Loading Models That Contain Custom ComponentsCustom Activation Functions, Initializers, Regularizers, and ConstraintsCustom MetricsCustom LayersCustom ModelsLosses and Metrics Based on Model InternalsComputing Gradients Using AutodiffCustom Training LoopsTensorFlow Functions and GraphsAutoGraph and TracingTF Function RulesExercises
13. Loading and Preprocessing Data with TensorFlow
The tf.data APIChaining TransformationsShuffling the DataInterleaving Lines from Multiple FilesPreprocessing the DataPutting Everything TogetherPrefetchingUsing the Dataset with KerasThe TFRecord FormatCompressed TFRecord FilesA Brief Introduction to Protocol BuffersTensorFlow ProtobufsLoading and Parsing ExamplesHandling Lists of Lists Using the SequenceExample ProtobufKeras Preprocessing LayersThe Normalization LayerThe Discretization LayerThe CategoryEncoding LayerThe StringLookup LayerThe Hashing LayerEncoding Categorical Features Using EmbeddingsText PreprocessingUsing Pretrained Language Model ComponentsImage Preprocessing LayersThe TensorFlow Datasets ProjectExercises
14. Deep Computer Vision Using Convolutional Neural Networks
The Architecture of the Visual CortexConvolutional LayersFiltersStacking Multiple Feature MapsImplementing Convolutional Layers with KerasMemory RequirementsPooling LayersImplementing Pooling Layers with KerasCNN ArchitecturesLeNet-5AlexNetGoogLeNetVGGNetResNetXceptionSENetOther Noteworthy ArchitecturesChoosing the Right CNN ArchitectureImplementing a ResNet-34 CNN Using KerasUsing Pretrained Models from KerasPretrained Models for Transfer LearningClassification and LocalizationObject DetectionFully Convolutional NetworksYou Only Look OnceObject TrackingSemantic SegmentationExercises
15. Processing Sequences Using RNNs and CNNs
Recurrent Neurons and LayersMemory CellsInput and Output SequencesTraining RNNsForecasting a Time SeriesThe ARMA Model FamilyPreparing the Data for Machine Learning ModelsForecasting Using a Linear ModelForecasting Using a Simple RNNForecasting Using a Deep RNNForecasting Multivariate Time SeriesForecasting Several Time Steps AheadForecasting Using a Sequence-to-Sequence ModelHandling Long SequencesFighting the Unstable Gradients ProblemTackling the Short-Term Memory ProblemExercises
16. Natural Language Processing with RNNs and Attention
Generating Shakespearean Text Using a Character RNNCreating the Training DatasetBuilding and Training the Char-RNN ModelGenerating Fake Shakespearean TextStateful RNNSentiment AnalysisMaskingReusing Pretrained Embeddings and Language ModelsAn Encoder–Decoder Network for Neural Machine TranslationBidirectional RNNsBeam SearchAttention MechanismsAttention Is All You Need: The Original Transformer ArchitectureAn Avalanche of Transformer ModelsVision TransformersHugging Face’s Transformers LibraryExercises
17. Autoencoders, GANs, and Diffusion Models
Efficient Data RepresentationsPerforming PCA with an Undercomplete Linear AutoencoderStacked AutoencodersImplementing a Stacked Autoencoder Using KerasVisualizing the ReconstructionsVisualizing the Fashion MNIST DatasetUnsupervised Pretraining Using Stacked AutoencodersTying WeightsTraining One Autoencoder at a TimeConvolutional AutoencodersDenoising AutoencodersSparse AutoencodersVariational AutoencodersGenerating Fashion MNIST ImagesGenerative Adversarial NetworksThe Difficulties of Training GANsDeep Convolutional GANsProgressive Growing of GANsStyleGANsDiffusion ModelsExercises
18. Reinforcement Learning
Learning to Optimize RewardsPolicy SearchIntroduction to OpenAI GymNeural Network PoliciesEvaluating Actions: The Credit Assignment ProblemPolicy GradientsMarkov Decision ProcessesTemporal Difference LearningQ-LearningExploration PoliciesApproximate Q-Learning and Deep Q-LearningImplementing Deep Q-LearningDeep Q-Learning VariantsFixed Q-value TargetsDouble DQNPrioritized Experience ReplayDueling DQNOverview of Some Popular RL AlgorithmsExercises
19. Training and Deploying TensorFlow Models at Scale
Serving a TensorFlow ModelUsing TensorFlow ServingCreating a Prediction Service on Vertex AIRunning Batch Prediction Jobs on Vertex AIDeploying a Model to a Mobile or Embedded DeviceRunning a Model in a Web PageUsing GPUs to Speed Up ComputationsGetting Your Own GPUManaging the GPU RAMPlacing Operations and Variables on DevicesParallel Execution Across Multiple DevicesTraining Models Across Multiple DevicesModel ParallelismData ParallelismTraining at Scale Using the Distribution Strategies APITraining a Model on a TensorFlow ClusterRunning Large Training Jobs on Vertex AIHyperparameter Tuning on Vertex AIExercisesThank You!
A. Machine Learning Project Checklist
Frame the Problem and Look at the Big PictureGet the DataExplore the DataPrepare the DataShortlist Promising ModelsFine-Tune the SystemPresent Your SolutionLaunch!
B. Autodiff
Manual DifferentiationFinite Difference ApproximationForward-Mode AutodiffReverse-Mode Autodiff
C. Special Data Structures
StringsRagged TensorsSparse TensorsTensor ArraysSetsQueues
D. TensorFlow Graphs
TF Functions and Concrete FunctionsExploring Function Definitions and GraphsA Closer Look at TracingUsing AutoGraph to Capture Control FlowHandling Variables and Other Resources in TF FunctionsUsing TF Functions with Keras (or Not)
Index
About the Author

Content preview from Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

Chapter 15. Processing Sequences Using RNNs and CNNs

Predicting the future is something you do all the time, whether you are finishing a friend’s sentence or anticipating the smell of coffee at breakfast. In this chapter we will discuss recurrent neural networks (RNNs)—a class of nets that can predict the future (well, up to a point). RNNs can analyze time series data, such as the number of daily active users on your website, the hourly temperature in your city, your home’s daily power consumption, the trajectories of nearby cars, and more. Once an RNN learns past patterns in the data, it is able to use its knowledge to forecast the future, assuming of course that past patterns still hold in the future.

More generally, RNNs can work on sequences of arbitrary lengths, rather than on fixed-sized inputs. For example, they can take sentences, documents, or audio samples as input, making them extremely useful for natural language processing applications such as automatic translation or speech-to-text.

In this chapter, we will first go through the fundamental concepts underlying RNNs and how to train them using backpropagation through time. Then, we will use them to forecast a time series. Along the way, we will look at the popular ARMA family of models, often used to forecast time series, and use them as baselines to compare with our RNNs. After that, we’ll explore the two main difficulties that RNNs face:

Unstable gradients (discussed in Chapter 11), which can be alleviated using ...

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition

by Aurélien Géron

Chapter 15. Processing Sequences Using RNNs and CNNs

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