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Chapter 1The Machine Learning Landscape What Is Machine Learning? Why Use Machine Learning? Types of Machine Learning Systems Main Challenges of Machine Learning Testing and Validating Exercises Chapter 2End-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 Chapter 3Classification MNIST Training a Binary Classifier Performance Measures Multiclass Classification Error Analysis Multilabel Classification Multioutput Classification Exercises Chapter 4Training Models Linear Regression Gradient Descent Polynomial Regression Learning Curves Regularized Linear Models Logistic Regression Exercises Chapter 5Support Vector Machines Linear SVM Classification Nonlinear SVM Classification SVM Regression Under the Hood Exercises Chapter 6Decision 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 Chapter 7Ensemble Learning and Random Forests Voting Classifiers Bagging and Pasting Random Patches and Random Subspaces Random Forests Boosting Stacking Exercises Chapter 8Dimensionality Reduction The Curse of Dimensionality Main Approaches for Dimensionality Reduction PCA Kernel PCA LLE Other Dimensionality Reduction Techniques Exercises Neural Networks and Deep Learning Chapter 9Up and Running with TensorFlow Installation Creating Your First Graph and Running It in a Session Managing Graphs Lifecycle of a Node Value Linear Regression with TensorFlow Implementing Gradient Descent Feeding Data to the Training Algorithm Saving and Restoring Models Visualizing the Graph and Training Curves Using TensorBoard Name Scopes Modularity Sharing Variables Exercises Chapter 10Introduction to Artificial Neural Networks From Biological to Artificial Neurons Training an MLP with TensorFlow’s High-Level API Training a DNN Using Plain TensorFlow Fine-Tuning Neural Network Hyperparameters Exercises Chapter 11Training Deep Neural Nets Vanishing/Exploding Gradients Problems Reusing Pretrained Layers Faster Optimizers Avoiding Overfitting Through Regularization Practical Guidelines Exercises Chapter 12Distributing TensorFlow Across Devices and Servers Multiple Devices on a Single Machine Multiple Devices Across Multiple Servers Parallelizing Neural Networks on a TensorFlow Cluster Exercises Chapter 13Convolutional Neural Networks The Architecture of the Visual Cortex Convolutional Layer Pooling Layer CNN Architectures Exercises Chapter 14Recurrent Neural Networks Recurrent Neurons Basic RNNs in TensorFlow Training RNNs Deep RNNs LSTM Cell GRU Cell Natural Language Processing Exercises Chapter 15Autoencoders Efficient Data Representations Performing PCA with an Undercomplete Linear Autoencoder Stacked Autoencoders Unsupervised Pretraining Using Stacked Autoencoders Denoising Autoencoders Sparse Autoencoders Variational Autoencoders Other Autoencoders Exercises Chapter 16Reinforcement 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 and Q-Learning Learning to Play Ms. Pac-Man Using Deep Q-Learning Exercises Thank You! Appendix Exercise Solutions Chapter 1: The Machine Learning Landscape Chapter 2: End-to-End Machine Learning Project Chapter 3: Classification Chapter 4: Training Linear Models Chapter 5: Support Vector Machines Chapter 6: Decision Trees Chapter 7: Ensemble Learning and Random Forests Chapter 8: Dimensionality Reduction Chapter 9: Up and Running with TensorFlow Chapter 10: Introduction to Artificial Neural Networks Chapter 11: Training Deep Neural Nets Chapter 12: Distributing TensorFlow Across Devices and Servers Chapter 13: Convolutional Neural Networks Chapter 14: Recurrent Neural Networks Chapter 15: Autoencoders Chapter 16: Reinforcement Learning Appendix Machine Learning Project Checklist Frame the Problem and Look at the Big Picture Get the Data Explore the Data Prepare the Data Short-List Promising Models Fine-Tune the System Present Your Solution Launch! Appendix SVM Dual Problem Appendix Autodiff Manual Differentiation Symbolic Differentiation Numerical Differentiation Forward-Mode Autodiff Reverse-Mode Autodiff Appendix Other Popular ANN Architectures Hopfield Networks Boltzmann Machines Restricted Boltzmann Machines Deep Belief Nets Self-Organizing Maps