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Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn

By : Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili
4.4 (95)
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Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn

4.4 (95)
By: Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili

Overview of this book

Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
Table of Contents (22 chapters)
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Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
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1
Giving Computers the Ability to Learn from Data
Giving Computers the Ability to Learn from Data
Building intelligent machines to transform data into knowledge
The three different types of machine learning
Introduction to the basic terminology and notations
A roadmap for building machine learning systems
Using Python for machine learning
Summary
2
Training Simple Machine Learning Algorithms for Classification
Training Simple Machine Learning Algorithms for Classification
Artificial neurons – a brief glimpse into the early history of machine learning
Implementing a perceptron learning algorithm in Python
Adaptive linear neurons and the convergence of learning
Summary
3
A Tour of Machine Learning Classifiers Using Scikit-Learn
A Tour of Machine Learning Classifiers Using Scikit-Learn
Choosing a classification algorithm
First steps with scikit-learn – training a perceptron
Modeling class probabilities via logistic regression
Maximum margin classification with support vector machines
Solving nonlinear problems using a kernel SVM
Decision tree learning
K-nearest neighbors – a lazy learning algorithm
Summary
4
Building Good Training Datasets – Data Preprocessing
Building Good Training Datasets – Data Preprocessing
Dealing with missing data
Handling categorical data
Partitioning a dataset into separate training and test datasets
Bringing features onto the same scale
Selecting meaningful features
Assessing feature importance with random forests
Summary
5
Compressing Data via Dimensionality Reduction
Compressing Data via Dimensionality Reduction
Unsupervised dimensionality reduction via principal component analysis
Supervised data compression via linear discriminant analysis
Nonlinear dimensionality reduction and visualization
Summary
6
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Learning Best Practices for Model Evaluation and Hyperparameter Tuning
Streamlining workflows with pipelines
Using k-fold cross-validation to assess model performance
Debugging algorithms with learning and validation curves
Fine-tuning machine learning models via grid search
Looking at different performance evaluation metrics
Summary
7
Combining Different Models for Ensemble Learning
Combining Different Models for Ensemble Learning
Learning with ensembles
Combining classifiers via majority vote
Bagging – building an ensemble of classifiers from bootstrap samples
Leveraging weak learners via adaptive boosting
Gradient boosting – training an ensemble based on loss gradients
Summary
8
Applying Machine Learning to Sentiment Analysis
Applying Machine Learning to Sentiment Analysis
Preparing the IMDb movie review data for text processing
Introducing the bag-of-words model
Training a logistic regression model for document classification
Working with bigger data – online algorithms and out-of-core learning
Topic modeling with latent Dirichlet allocation
Summary
9
Predicting Continuous Target Variables with Regression Analysis
Predicting Continuous Target Variables with Regression Analysis
Introducing linear regression
Exploring the Ames Housing dataset
Implementing an ordinary least squares linear regression model
Fitting a robust regression model using RANSAC
Evaluating the performance of linear regression models
Using regularized methods for regression
Turning a linear regression model into a curve – polynomial regression
Dealing with nonlinear relationships using random forests
Summary
10
Working with Unlabeled Data – Clustering Analysis
Working with Unlabeled Data – Clustering Analysis
Grouping objects by similarity using k-means
Organizing clusters as a hierarchical tree
Locating regions of high density via DBSCAN
Summary
11
Implementing a Multilayer Artificial Neural Network from Scratch
Implementing a Multilayer Artificial Neural Network from Scratch
Modeling complex functions with artificial neural networks
Classifying handwritten digits
Training an artificial neural network
About convergence in neural networks
A few last words about the neural network implementation
Summary
12
Parallelizing Neural Network Training with PyTorch
Parallelizing Neural Network Training with PyTorch
PyTorch and training performance
First steps with PyTorch
Building input pipelines in PyTorch
Building an NN model in PyTorch
Choosing activation functions for multilayer neural networks
Summary
13
Going Deeper – The Mechanics of PyTorch
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Going Deeper – The Mechanics of PyTorch
The key features of PyTorch
PyTorch’s computation graphs
PyTorch tensor objects for storing and updating model parameters
Computing gradients via automatic differentiation
Simplifying implementations of common architectures via the torch.nn module
Project one – predicting the fuel efficiency of a car
Project two – classifying MNIST handwritten digits
Higher-level PyTorch APIs: a short introduction to PyTorch-Lightning
Summary
14
Classifying Images with Deep Convolutional Neural Networks
Classifying Images with Deep Convolutional Neural Networks
The building blocks of CNNs
Putting everything together – implementing a CNN
Implementing a deep CNN using PyTorch
Smile classification from face images using a CNN
Summary
15
Modeling Sequential Data Using Recurrent Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
Introducing sequential data
RNNs for modeling sequences
Implementing RNNs for sequence modeling in PyTorch
Summary
16
Transformers – Improving Natural Language Processing with Attention Mechanisms
Transformers – Improving Natural Language Processing with Attention Mechanisms
Adding an attention mechanism to RNNs
Introducing the self-attention mechanism
Attention is all we need: introducing the original transformer architecture
Building large-scale language models by leveraging unlabeled data
Fine-tuning a BERT model in PyTorch
Summary
17
Generative Adversarial Networks for Synthesizing New Data
Generative Adversarial Networks for Synthesizing New Data
Introducing generative adversarial networks
Implementing a GAN from scratch
Improving the quality of synthesized images using a convolutional and Wasserstein GAN
Other GAN applications
Summary
18
Graph Neural Networks for Capturing Dependencies in Graph Structured Data
Graph Neural Networks for Capturing Dependencies in Graph Structured Data
Introduction to graph data
Understanding graph convolutions
Implementing a GNN in PyTorch from scratch
Implementing a GNN using the PyTorch Geometric library
Other GNN layers and recent developments
Summary
19
Reinforcement Learning for Decision Making in Complex Environments
Reinforcement Learning for Decision Making in Complex Environments
Introduction – learning from experience
The theoretical foundations of RL
Reinforcement learning algorithms
Implementing our first RL algorithm
A glance at deep Q-learning
Chapter and book summary
20
Other Books You May Enjoy
Other Books You May Enjoy
21
Index
Index

PyTorch’s computation graphs

PyTorch performs its computations based on a directed acyclic graph (DAG). In this section, we will see how these graphs can be defined for a simple arithmetic computation. Then, we will see the dynamic graph paradigm, as well as how the graph is created on the fly in PyTorch.

Understanding computation graphs

PyTorch relies on building a computation graph at its core, and it uses this computation graph to derive relationships between tensors from the input all the way to the output. Let’s say that we have rank 0 (scalar) tensors a, b, and c and we want to evaluate z = 2 × (a – b) + c.

This evaluation can be represented as a computation graph, as shown in Figure 13.1:

Figure 13.1: How a computation graph works

As you can see, the computation graph is simply a network of nodes. Each node resembles an operation, which applies a function to its input tensor or tensors...

End of Section 3
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