You're reading from Machine Learning with PyTorch and Scikit-Learn Develop machine learning and deep learning models with Python

Product type Paperback

Published in Feb 2022

Publisher Packt

ISBN-13 9781801819312

Length 774 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Authors (3):

Sebastian Raschka

Yuxi (Hayden) Liu

Vahid Mirjalili

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Table of Contents (22) Chapters

Preface

1. Giving Computers the Ability to Learn from Data FREE CHAPTER

2. Training Simple Machine Learning Algorithms for Classification

3. A Tour of Machine Learning Classifiers Using Scikit-Learn

4. Building Good Training Datasets – Data Preprocessing

5. Compressing Data via Dimensionality Reduction

6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning

7. Combining Different Models for Ensemble Learning

8. Applying Machine Learning to Sentiment Analysis

9. Predicting Continuous Target Variables with Regression Analysis

10. Working with Unlabeled Data – Clustering Analysis

11. Implementing a Multilayer Artificial Neural Network from Scratch

12. Parallelizing Neural Network Training with PyTorch

13. Going Deeper – The Mechanics of PyTorch

14. Classifying Images with Deep Convolutional Neural Networks

15. Modeling Sequential Data Using Recurrent Neural Networks

16. Transformers – Improving Natural Language Processing with Attention Mechanisms

17. Generative Adversarial Networks for Synthesizing New Data

18. Graph Neural Networks for Capturing Dependencies in Graph Structured Data

19. Reinforcement Learning for Decision Making in Complex Environments

20. Other Books You May Enjoy

21. Index

Index

Symbols

5×2 cross-validation 192

7-Zip

URL 248

accuracy

versus classification error 57

action-value function 682

estimation, with Monte Carlo 688

greedy policy, computing from 689

activation function, for multilayer neural network

selecting 400

activation functions, torch.nn module

reference link 406

activations

computing, in RNNs 504, 505

AdaBoost

applying, with scikit-learn 233-236

comparing, with gradient boosting 237

AdaBoost recognition 229

Adam optimizer 479

adaptive boosting

weak learners, leveraging 229

working 229-233

Adaptive Linear Neuron (Adaline) 35-37, 278

algorithm 337

implementation, converting into algorithm for logistic regression 66-68

implementing, in Python 39-43

advanced graph neural network literature

pointers 669, 670

agent 6, 674, 675

agglomerative clustering

applying, via scikit-learn 327, 328

AI winters

reference...

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Table of Contents (22) Chapters

Index

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Authors (3)

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