You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

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Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

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

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

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

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Introducing sequential learning

The machine learning problems we have solved so far in this book have been time-independent. For example, ad click-through doesn't depend on the user's historical ad clicks under our previous approach; in face classification, the model only takes in the current face image, not previous ones. However, there are many cases in life that depend on time. For example, in financial fraud detection, we can't just look at the present transaction; we should also consider previous transactions so that we can model based on their discrepancy. Another example is part-of-speech (PoS) tagging, where we assign a PoS (verb, noun, adverb, and so on) to a word. Instead of solely focusing on the given word, we must look at some previous words, and sometimes the next words too.

In time-dependent cases like those just mentioned, the current output is dependent on not only the current input, but also the previous inputs; note that the length of the ...