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Machine Learning with PyTorch and Scikit-Learn

You're reading from  Machine Learning with PyTorch and Scikit-Learn

Product type Book
Published in Feb 2022
Publisher Packt
ISBN-13 9781801819312
Pages 774 pages
Edition 1st Edition
Languages
Authors (3):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Profile icon Yuxi (Hayden) Liu
Vahid Mirjalili Vahid Mirjalili
Profile icon Vahid Mirjalili
View More author details

Table of Contents (22) Chapters

Preface 1. Giving Computers the Ability to Learn from Data 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

A

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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Machine Learning with PyTorch and Scikit-Learn
Published in: Feb 2022 Publisher: Packt ISBN-13: 9781801819312
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