Cover -- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow -- Copyright -- Table of Contents -- Preface -- PART I The Fundamentals of Machine Learning -- 1 The Machine Learning Landscape -- What Is Machine Learning? -- Why Use Machine Learning? -- Examples of Applications -- Types of Machine Learning Systems -- Main Challenges of Machine Learning -- Testing and Validating -- Exercises -- 2 End-to-End Machine Learning Project -- Working with Real Data -- Look at the Big Picture -- Get the Data -- Discover and Visualize the Data to Gain Insights -- Prepare the Data for Machine Learning Algorithms -- Select and Train a Model -- Fine-Tune Your Model -- Launch, Monitor, and Maintain Your System -- Try It Out! -- Exercises -- 3 Classification -- MNIST -- Training a Binary Classifier -- Performance Measures -- Multiclass Classification -- Error Analysis -- Multilabel Classification -- Multioutput Classification -- Exercises -- 4 Training Models -- Linear Regression -- Gradient Descent -- Polynomial Regression -- Learning Curves -- Regularized Linear Models -- Logistic Regression -- Exercises -- 5 Support Vector Machines -- Linear SVM Classification -- Nonlinear SVM Classification -- SVM Regression -- Under the Hood -- Exercises -- 6 Decision Trees -- Training and Visualizing a Decision Tree -- Making Predictions -- Estimating Class Probabilities -- The CART Training Algorithm -- Computational Complexity -- Gini Impurity or Entropy? -- Regularization Hyperparameters -- Regression -- Instability -- Exercises -- 7 Ensemble Learning and Random Forests -- Voting Classifiers -- Bagging and Pasting -- Random Patches and Random Subspaces -- Random Forests -- Boosting -- Stacking -- Exercises -- 8 Dimensionality Reduction -- The Curse of Dimensionality -- Main Approaches for Dimensionality Reduction -- PCA -- Kernel PCA -- LLE.

Other Dimensionality Reduction Techniques -- Exercises -- 9 Unsupervised Learning Techniques -- Clustering -- Gaussian Mixtures -- Exercises -- PART II Neural Networks and Deep Learning -- 10 Introduction to Artificial Neural Networks with Keras -- From Biological to Artificial Neurons -- Implementing MLPs with Keras -- Fine-Tuning Neural Network Hyperparameters -- Exercises -- 11 Training Deep Neural Networks -- The Vanishing/Exploding Gradients Problems -- Reusing Pretrained Layers -- Faster Optimizers -- Avoiding Overfitting Through Regularization -- Summary and Practical Guidelines -- Exercises -- 12 Custom Models and Training with TensorFlow -- A Quick Tour of TensorFlow -- Using TensorFlow like NumPy -- Customizing Models and Training Algorithms -- TensorFlow Functions and Graphs -- Exercises -- 13 Loading and Preprocessing Data with TensorFlow -- The Data API -- The TFRecord Format -- Preprocessing the Input Features -- TF Transform -- The TensorFlow Datasets (TFDS) Project -- Exercises -- 14 Deep Computer Vision Using Convolutional Neural Networks -- The Architecture of the Visual Cortex -- Convolutional Layers -- Pooling Layers -- CNN Architectures -- Implementing a ResNet-34 CNN Using Keras -- Using Pretrained Models from Keras -- Pretrained Models for Transfer Learning -- Classification and Localization -- Object Detection -- Semantic Segmentation -- Exercises -- 15 Processing Sequences Using RNNs and CNNs -- Recurrent Neurons and Layers -- Training RNNs -- Forecasting a Time Series -- Handling Long Sequences -- Exercises -- 16 Natural Language Processing with RNNs and Attention -- Generating Shakespearean Text Using a Character RNN -- Sentiment Analysis -- An Encoder-Decoder Network for Neural Machine Translation -- Attention Mechanisms -- Recent Innovations in Language Models -- Exercises.

17 Representation Learning and Generative Learning Using Autoencoders and GANs -- Efficient Data Representations -- Performing PCA with an Undercomplete Linear Autoencoder -- Stacked Autoencoders -- Convolutional Autoencoders -- Recurrent Autoencoders -- Denoising Autoencoders -- Sparse Autoencoders -- Variational Autoencoders -- Generative Adversarial Networks -- Exercises -- 18 Reinforcement Learning -- Learning to Optimize Rewards -- Policy Search -- Introduction to OpenAI Gym -- Neural Network Policies -- Evaluating Actions: The Credit Assignment Problem -- Policy Gradients -- Markov Decision Processes -- Temporal Difference Learning -- Q-Learning -- Implementing Deep Q-Learning -- Deep Q-Learning Variants -- The TF-Agents Library -- Overview of Some Popular RL Algorithms -- Exercises -- 19 Training and Deploying TensorFlow Models at Scale -- Serving a TensorFlow Model -- Deploying a Model to a Mobile or Embedded Device -- Using GPUs to Speed Up Computations -- Training Models Across Multiple Devices -- Exercises -- Thank You! -- Appendix A: Exercise Solutions -- Appendix B: Machine Learning Project Checklist -- Appendix C: SVM Dual Problem -- Appendix D: Autodiff -- Appendix E: Other Popular ANN Architectures -- Appendix F: Special Data Structures -- Appendix G: TensorFlow Graphs -- Index.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2025. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

Licensed e-book