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

View More author details

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

Modeling class probabilities via logistic regression

Although the perceptron rule offers a nice and easy-going introduction to machine learning algorithms for classification, its biggest disadvantage is that it never converges if the classes are not perfectly linearly separable. The classification task in the previous section would be an example of such a scenario. The reason for this is that the weights are continuously being updated since there is always at least one misclassified training example present in each epoch. Of course, you can change the learning rate and increase the number of epochs, but be warned that the perceptron will never converge on this dataset.

To make better use of our time, we will now take a look at another simple, yet more powerful, algorithm for linear and binary classification problems: logistic regression. Note that, despite its name, logistic regression is a model for classification, not regression.