Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Preface....28 Who this book is for....30 What this book covers....31 To get the most out of this book....35 Get in touch....38 Share your thoughts....39 Giving Computers the Ability to Learn from Data....40 Building intelligent machines to transform data into knowledge....41 The three different types of machine learning....42 Making predictions about the future with supervised learning....43 Classification for predicting class labels....44 Regression for predicting continuous outcomes....46 Solving interactive problems with reinforcement learning....48 Discovering hidden structures with unsupervised learning....50 Finding subgroups with clustering....50 Dimensionality reduction for data compression....51 Introduction to the basic terminology and notations....52 Notation and conventions used in this book....53 Machine learning terminology....55 A roadmap for building machine learning systems....56 Preprocessing – getting data into shape....57 Training and selecting a predictive model....58 Evaluating models and predicting unseen data instances....59 Using Python for machine learning....60 Installing Python and packages from the Python Package Index....61 Using the Anaconda Python distribution and package manager....62 Packages for scientific computing, data science, and machine learning....64 Summary....66 Training Simple Machine Learning Algorithms for Classification....68 Artificial neurons – a brief glimpse into the early history of machine learning....68 The formal definition of an artificial neuron....70 The perceptron learning rule....72 Implementing a perceptron learning algorithm in Python....75 An object-oriented perceptron API....76 Training a perceptron model on the Iris dataset....80 Adaptive linear neurons and the convergence of learning....87 Minimizing loss functions with gradient descent....88 Implementing Adaline in Python....91 Improving gradient descent through feature scaling....96 Large-scale machine learning and stochastic gradient descent....98 Summary....104 A Tour of Machine Learning Classifiers Using Scikit-Learn....106 Choosing a classification algorithm....106 First steps with scikit-learn – training a perceptron....108 Modeling class probabilities via logistic regression....115 Logistic regression and conditional probabilities....116 Learning the model weights via the logistic loss function....121 Converting an Adaline implementation into an algorithm for logistic regression....125 Training a logistic regression model with scikit-learn....129 Tackling overfitting via regularization....133 Maximum margin classification with support vector machines....138 Maximum margin intuition....139 Dealing with a nonlinearly separable case using slack variables....140 Alternative implementations in scikit-learn....142 Solving nonlinear problems using a kernel SVM....143 Kernel methods for linearly inseparable data....143 Using the kernel trick to find separating hyperplanes in a high-dimensional space....145 Decision tree learning....149 Maximizing IG – getting the most bang for your buck....151 Building a decision tree....156 Combining multiple decision trees via random forests....159 K-nearest neighbors – a lazy learning algorithm....164 Summary....170 Building Good Training Datasets – Data Preprocessing....172 Dealing with missing data....172 Identifying missing values in tabular data....173 Eliminating training examples or features with missing values....175 Imputing missing values....177 Understanding the scikit-learn estimator API....179 Handling categorical data....181 Categorical data encoding with pandas....182 Mapping ordinal features....182 Encoding class labels....184 Performing one-hot encoding on nominal features....185 Optional: encoding ordinal features....190 Partitioning a dataset into separate training and test datasets....191 Bringing features onto the same scale....196 Selecting meaningful features....200 L1 and L2 regularization as penalties against model complexity....201 A geometric interpretation of L2 regularization....202 Sparse solutions with L1 regularization....205 Sequential feature selection algorithms....210 Assessing feature importance with random forests....219 Summary....222 Compressing Data via Dimensionality Reduction....224 Unsupervised dimensionality reduction via principal component analysis....225 The main steps in principal component analysis....225 Extracting the principal components step by step....229 Total and explained variance....233 Feature transformation....235 Principal component analysis in scikit-learn....240 Assessing feature contributions....244 Supervised data compression via linear discriminant analysis....247 Principal component analysis versus linear discriminant analysis....247 The inner workings of linear discriminant analysis....249 Computing the scatter matrices....250 Selecting linear discriminants for the new feature subspace....254 Projecting examples onto the new feature space....257 LDA via scikit-learn....258 Nonlinear dimensionality reduction and visualization....260 Why consider nonlinear dimensionality reduction?....261 Visualizing data via t-distributed stochastic neighbor embedding....263 Summary....268 Learning Best Practices for Model Evaluation and Hyperparameter Tuning....270 Streamlining workflows with pipelines....270 Loading the Breast Cancer Wisconsin dataset....271 Combining transformers and estimators in a pipeline....274 Using k-fold cross-validation to assess model performance....277 The holdout method....277 K-fold cross-validation....279 Debugging algorithms with learning and validation curves....286 Diagnosing bias and variance problems with learning curves....286 Addressing over- and underfitting with validation curves....290 Fine-tuning machine learning models via grid search....293 Tuning hyperparameters via grid search....293 Exploring hyperparameter configurations more widely with randomized search....296 More resource-efficient hyperparameter search with successive halving....299 Algorithm selection with nested cross-validation....303 Looking at different performance evaluation metrics....306 Reading a confusion matrix....306 Optimizing the precision and recall of a classification model....309 Plotting a receiver operating characteristic....313 Scoring metrics for multiclass classification....317 Dealing with class imbalance....318 Summary....323 Combining Different Models for Ensemble Learning....325 Learning with ensembles....325 Combining classifiers via majority vote....331 Implementing a simple majority vote classifier....331 Using the majority voting principle to make predictions....338 Evaluating and tuning the ensemble classifier....342 Bagging – building an ensemble of classifiers from bootstrap samples....350 Bagging in a nutshell....351 Applying bagging to classify examples in the Wine dataset....353 Leveraging weak learners via adaptive boosting....358 How adaptive boosting works....359 Applying AdaBoost using scikit-learn....366 Gradient boosting – training an ensemble based on loss gradients....370 Comparing AdaBoost with gradient boosting....371 Outlining the general gradient boosting algorithm....372 Explaining the gradient boosting algorithm for classification....375 Illustrating gradient boosting for classification....378 Using XGBoost....381 Summary....384 Applying Machine Learning to Sentiment Analysis....386 Preparing the IMDb movie review data for text processing....386 Obtaining the movie review dataset....387 Preprocessing the movie dataset into a more convenient format....388 Introducing the bag-of-words model....390 Transforming words into feature vectors....391 Assessing word relevancy via term frequency-inverse document frequency....394 Cleaning text data....397 Processing documents into tokens....400 Training a logistic regression model for document classification....403 Working with bigger data – online algorithms and out-of-core learning....407 Topic modeling with latent Dirichlet allocation....412 Decomposing text documents with LDA....413 LDA with scikit-learn....414 Summary....418 Predicting Continuous Target Variables with Regression Analysis....420 Introducing linear regression....421 Simple linear regression....421 Multiple linear regression....422 Exploring the Ames Housing dataset....424 Loading the Ames Housing dataset into a DataFrame....424 Visualizing the important characteristics of a dataset....428 Looking at relationships using a correlation matrix....430 Implementing an ordinary least squares linear regression model....433 Solving regression for regression parameters with gradient descent....434 Estimating the coefficient of a regression model via scikit-learn....439 Fitting a robust regression model using RANSAC....443 Evaluating the performance of linear regression models....447 Using regularized methods for regression....454 Turning a linear regression model into a curve – polynomial regression....457 Adding polynomial terms using scikit-learn....457 Modeling nonlinear relationships in the Ames Housing dataset....460 Dealing with nonlinear relationships using random forests....463 Decision tree regression....464 Random forest regression....467 Summary....470 Working with Unlabeled Data – Clustering Analysis....472 Grouping objects by similarity using k-means....472 k-means clustering using scikit-learn....473 A smarter way of placing the initial cluster centroids using k-means++....479 Hard versus soft clustering....481 Using the elbow method to find the optimal number of clusters....483 Quantifying the quality of clustering via silhouette plots....485 Organizing clusters as a hierarchical tree....490 Grouping clusters in a bottom-up fashion....491 Performing hierarchical clustering on a distance matrix....493 Attaching dendrograms to a heat map....498 Applying agglomerative clustering via scikit-learn....500 Locating regions of high density via DBSCAN....501 Summary....508 Implementing a Multilayer Artificial Neural Network from Scratch....511 Modeling complex functions with artificial neural networks....511 Single-layer neural network recap....513 Introducing the multilayer neural network architecture....516 Activating a neural network via forward propagation....519 Classifying handwritten digits....522 Obtaining and preparing the MNIST dataset....523 Implementing a multilayer perceptron....527 Coding the neural network training loop....533 Evaluating the neural network performance....539 Training an artificial neural network....544 Computing the loss function....545 Developing your understanding of backpropagation....547 Training neural networks via backpropagation....549 About convergence in neural networks....554 A few last words about the neural network implementation....556 Summary....557 Parallelizing Neural Network Training with PyTorch....559 PyTorch and training performance....560 Performance challenges....560 What is PyTorch?....562 How we will learn PyTorch....564 First steps with PyTorch....565 Installing PyTorch....565 Creating tensors in PyTorch....567 Manipulating the data type and shape of a tensor....568 Applying mathematical operations to tensors....570 Split, stack, and concatenate tensors....572 Building input pipelines in PyTorch....575 Creating a PyTorch DataLoader from existing tensors....576 Combining two tensors into a joint dataset....577 Shuffle, batch, and repeat....579 Creating a dataset from files on your local storage disk....581 Fetching available datasets from the torchvision.datasets library....586 Building an NN model in PyTorch....592 The PyTorch neural network module (torch.nn)....593 Building a linear regression model....594 Model training via the torch.nn and torch.optim modules....598 Building a multilayer perceptron for classifying flowers in the Iris dataset....600 Evaluating the trained model on the test dataset....604 Saving and reloading the trained model....605 Choosing activation functions for multilayer neural networks....606 Logistic function recap....608 Estimating class probabilities in multiclass classification via the softmax function....610 Broadening the output spectrum using a hyperbolic tangent....612 Rectified linear unit activation....615 Summary....617 Going Deeper – The Mechanics of PyTorch....619 The key features of PyTorch....620 PyTorch’s computation graphs....621 Understanding computation graphs....622 Creating a graph in PyTorch....623 PyTorch tensor objects for storing and updating model parameters....624 Computing gradients via automatic differentiation....628 Computing the gradients of the loss with respect to trainable variables....628 Understanding automatic differentiation....631 Adversarial examples....631 Simplifying implementations of common architectures via the torch.nn module....632 Implementing models based on nn.Sequential....632 Choosing a loss function....634 Solving an XOR classification problem....636 Making model building more flexible with nn.Module....642 Writing custom layers in PyTorch....645 Project one – predicting the fuel efficiency of a car....650 Working with feature columns....651 Training a DNN regression model....656 Project two – classifying MNIST handwritten digits....659 Higher-level PyTorch APIs: a short introduction to PyTorch-Lightning....663 Setting up the PyTorch Lightning model....665 Setting up the data loaders for Lightning....668 Training the model using the PyTorch Lightning Trainer class....670 Evaluating the model using TensorBoard....671 Summary....677 Classifying Images with Deep Convolutional Neural Networks....679 The building blocks of CNNs....679 Understanding CNNs and feature hierarchies....680 Performing discrete convolutions....683 Discrete convolutions in one dimension....683 Padding inputs to control the size of the output feature maps....686 Determining the size of the convolution output....688 Performing a discrete convolution in 2D....689 Subsampling layers....693 Putting everything together – implementing a CNN....695 Working with multiple input or color channels....696 Regularizing an NN with L2 regularization and dropout....700 Loss functions for classification....704 Implementing a deep CNN using PyTorch....707 The multilayer CNN architecture....707 Loading and preprocessing the data....708 Implementing a CNN using the torch.nn module....709 Configuring CNN layers in PyTorch....710 Constructing a CNN in PyTorch....711 Smile classification from face images using a CNN....717 Loading the CelebA dataset....717 Image transformation and data augmentation....719 Training a CNN smile classifier....726 Summary....732 Modeling Sequential Data Using Recurrent Neural Networks....734 Introducing sequential data....735 Modeling sequential data – order matters....735 Sequential data versus time series data....736 Representing sequences....737 The different categories of sequence modeling....738 RNNs for modeling sequences....740 Understanding the dataflow in RNNs....740 Computing activations in an RNN....744 Hidden recurrence versus output recurrence....747 The challenges of learning long-range interactions....751 Long short-term memory cells....753 Implementing RNNs for sequence modeling in PyTorch....756 Project one – predicting the sentiment of IMDb movie reviews....757 Preparing the movie review data....757 Embedding layers for sentence encoding....764 Building an RNN model....767 Building an RNN model for the sentiment analysis task....769 Project two – character-level language modeling in PyTorch....775 Preprocessing the dataset....776 Building a character-level RNN model....782 Evaluation phase – generating new text passages....785 Summary....791 Transformers – Improving Natural Language Processing with Attention Mechanisms....793 Adding an attention mechanism to RNNs....794 Attention helps RNNs with accessing information....795 The original attention mechanism for RNNs....796 Processing the inputs using a bidirectional RNN....798 Generating outputs from context vectors....799 Computing the attention weights....800 Introducing the self-attention mechanism....802 Starting with a basic form of self-attention....803 Parameterizing the self-attention mechanism: scaled dot-product attention....809 Attention is all we need: introducing the original transformer architecture....813 Encoding context embeddings via multi-head attention....815 Learning a language model: decoder and masked multi-head attention....823 Implementation details: positional encodings and layer normalization....825 Building large-scale language models by leveraging unlabeled data....828 Pre-training and fine-tuning transformer models....828 Leveraging unlabeled data with GPT....832 Using GPT-2 to generate new text....839 Bidirectional pre-training with BERT....843 The best of both worlds: BART....849 Fine-tuning a BERT model in PyTorch....853 Loading the IMDb movie review dataset....854 Tokenizing the dataset....857 Loading and fine-tuning a pre-trained BERT model....859 Fine-tuning a transformer more conveniently using the Trainer API....864 Summary....869 Generative Adversarial Networks for Synthesizing New Data....872 Introducing generative adversarial networks....872 Starting with autoencoders....874 Generative models for synthesizing new data....877 Generating new samples with GANs....879 Understanding the loss functions of the generator and discriminator networks in a GAN model....881 Implementing a GAN from scratch....884 Training GAN models on Google Colab....884 Implementing the generator and the discriminator networks....888 Defining the training dataset....892 Training the GAN model....895 Improving the quality of synthesized images using a convolutional and Wasserstein GAN....902 Transposed convolution....903 Batch normalization....905 Implementing the generator and discriminator....908 Dissimilarity measures between two distributions....916 Using EM distance in practice for GANs....921 Gradient penalty....922 Implementing WGAN-GP to train the DCGAN model....923 Mode collapse....928 Other GAN applications....930 Summary....931 Graph Neural Networks for Capturing Dependencies in Graph Structured Data....933 Introduction to graph data....934 Undirected graphs....935 Directed graphs....936 Labeled graphs....937 Representing molecules as graphs....938 Understanding graph convolutions....939 The motivation behind using graph convolutions....939 Implementing a basic graph convolution....943 Implementing a GNN in PyTorch from scratch....948 Defining the NodeNetwork model....949 Coding the NodeNetwork’s graph convolution layer....951 Adding a global pooling layer to deal with varying graph sizes....952 Preparing the DataLoader....956 Using the NodeNetwork to make predictions....959 Implementing a GNN using the PyTorch Geometric library....961 Other GNN layers and recent developments....969 Spectral graph convolutions....970 Pooling....973 Normalization....975 Pointers to advanced graph neural network literature....978 Summary....980 Reinforcement Learning for Decision Making in Complex Environments....983 Introduction – learning from experience....984 Understanding reinforcement learning....984 Defining the agent-environment interface of a reinforcement learning system....987 The theoretical foundations of RL....989 Markov decision processes....989 The mathematical formulation of Markov decision processes....991 Visualization of a Markov process....993 Episodic versus continuing tasks....994 RL terminology: return, policy, and value function....995 The return....995 Policy....998 Value function....998 Dynamic programming using the Bellman equation....1001 Reinforcement learning algorithms....1002 Dynamic programming....1003 Policy evaluation – predicting the value function with dynamic programming....1004 Improving the policy using the estimated value function....1005 Policy iteration....1006 Value iteration....1007 Reinforcement learning with Monte Carlo....1008 State-value function estimation using MC....1009 Action-value function estimation using MC....1009 Finding an optimal policy using MC control....1010 Policy improvement – computing the greedy policy from the action-value function....1010 Temporal difference learning....1011 TD prediction....1011 On-policy TD control (SARSA)....1013 Off-policy TD control (Q-learning)....1014 Implementing our first RL algorithm....1015 Introducing the OpenAI Gym toolkit....1015 Working with the existing environments in OpenAI Gym....1016 A grid world example....1018 Implementing the grid world environment in OpenAI Gym....1019 Solving the grid world problem with Q-learning....1027 A glance at deep Q-learning....1031 Training a DQN model according to the Q-learning algorithm....1033 Replay memory....1033 Determining the target values for computing the loss....1035 Implementing a deep Q-learning algorithm....1036 Chapter and book summary....1041 Other Books You May Enjoy....1047 Index....1052