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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

K-nearest neighbors – a lazy learning algorithm

The last supervised learning algorithm that we want to discuss in this chapter is the k-nearest neighbor (KNN) classifier, which is particularly interesting because it is fundamentally different from the learning algorithms that we have discussed so far.

KNN is a typical example of a lazy learner. It is called “lazy” not because of its apparent simplicity, but because it doesn’t learn a discriminative function from the training data but memorizes the training dataset instead.

Parametric versus non-parametric models

Machine learning algorithms can be grouped into parametric and non-parametric models. Using parametric models, we estimate parameters from the training dataset to learn a function that can classify new data points without requiring the original training dataset anymore. Typical examples of parametric models are the perceptron, logistic regression, and the linear SVM. In contrast...

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