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Machine Learning with PyTorch and Scikit-Learn

You're reading from   Machine Learning with PyTorch and Scikit-Learn Develop machine learning and deep learning models with Python

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Product type Paperback
Published in Feb 2022
Publisher Packt
ISBN-13 9781801819312
Length 774 pages
Edition 1st Edition
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Authors (3):
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Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
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Toc

Table of Contents (22) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification FREE CHAPTER 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

Bringing features onto the same scale

Feature scaling is a crucial step in our preprocessing pipeline that can easily be forgotten. Decision trees and random forests are two of the very few machine learning algorithms where we don’t need to worry about feature scaling. Those algorithms are scale-invariant. However, the majority of machine learning and optimization algorithms behave much better if features are on the same scale, as we saw in Chapter 2, Training Simple Machine Learning Algorithms for Classification, when we implemented the gradient descent optimization algorithm.

The importance of feature scaling can be illustrated by a simple example. Let’s assume that we have two features where one feature is measured on a scale from 1 to 10 and the second feature is measured on a scale from 1 to 100,000, respectively.

When we think of the squared error function in Adaline from Chapter 2, it makes sense to say that the algorithm will mostly be busy optimizing...

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