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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

A few last words about the neural network implementation

You may be wondering why we went through all of this theory just to implement a simple multilayer artificial network that can classify handwritten digits instead of using an open source Python machine learning library. In fact, we will introduce more complex NN models in the next chapters, which we will train using the open source PyTorch library (https://pytorch.org).

Although the from-scratch implementation in this chapter seems a bit tedious at first, it was a good exercise for understanding the basics behind backpropagation and NN training. A basic understanding of algorithms is crucial for applying machine learning techniques appropriately and successfully.

Now that you have learned how feedforward NNs work, we are ready to explore more sophisticated DNNs using PyTorch, which allows us to construct NNs more efficiently, as we will see in Chapter 12, Parallelizing Neural Network Training with PyTorch.

PyTorch...

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