You're reading from Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (18) Chapters

Preface

1. Getting Started with Machine Learning and Python FREE CHAPTER

2. Building a Movie Recommendation Engine with Naïve Bayes

3. Predicting Online Ad Click-Through with Tree-Based Algorithms

4. Predicting Online Ad Click-Through with Logistic Regression

5. Predicting Stock Prices with Regression Algorithms

6. Predicting Stock Prices with Artificial Neural Networks

7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

8. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

9. Recognizing Faces with Support Vector Machine

10. Machine Learning Best Practices

11. Categorizing Images of Clothing with Convolutional Neural Networks

12. Making Predictions with Sequences Using Recurrent Neural Networks

13. Advancing Language Understanding and Generation with the Transformer Models

14. Building an Image Search Engine Using CLIP: a Multimodal Approach

15. Making Decisions in Complex Environments with Reinforcement Learning

16. Other Books You May Enjoy

17. Index

Training a logistic regression model

Now, the question is as follows: how can we obtain the optimal w such that J(w) is minimized? We can do so using gradient descent.

Training a logistic regression model using gradient descent

Gradient descent (also called steepest descent) is a procedure for minimizing a loss function by first-order iterative optimization. In each iteration, the model parameters move a small step that is proportional to the negative derivative of the objective function at the current point. This means the to-be-optimal point iteratively moves downhill toward the minimal value of the objective function. The proportion we just mentioned is called the learning rate, or step size. It can be summarized in a mathematical equation as follows:

Here, the left w is the weight vector after a learning step, and the right w is the one before moving, is the learning rate, and is the first-order derivative, the gradient.