Helpful installation and setup instructions can be found in the README.md file of Chapter 1
- Machine Learning - Giving Computers the Ability to Learn from Data [open dir]
- Training Machine Learning Algorithms for Classification [open dir]
- A Tour of Machine Learning Classifiers Using Scikit-Learn [open dir]
- Building Good Training Sets – Data Pre-Processing [open dir]
- Compressing Data via Dimensionality Reduction [open dir]
- Learning Best Practices for Model Evaluation and Hyperparameter Optimization [open dir]
- Combining Different Models for Ensemble Learning [open dir]
- Applying Machine Learning to Sentiment Analysis [open dir]
- Predicting Continuous Target Variables with Regression Analysis [open dir]
- Working with Unlabeled Data – Clustering Analysis [open dir]
- Implementing a Multi-layer Artificial Neural Network from Scratch [open dir]
- Parallelizing Neural Network Training with PyTorch [open dir]
- Going Deeper -- The Mechanics of PyTorch [open dir]
- Classifying Images with Deep Convolutional Neural Networks [open dir]
- Modeling Sequential Data Using Recurrent Neural Networks [open dir]
- Transformers -- Improving Natural Language Processing with Attention Mechanisms [open dir]
- Generative Adversarial Networks for Synthesizing New Data [open dir]
- Graph Neural Networks for Capturing Dependencies in Graph Structured Data [open dir]
- Reinforcement Learning for Decision Making in Complex Environments [open dir]
Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili. Machine Learning with PyTorch and Scikit-Learn. Packt Publishing, 2022.
@book{mlbook2022,
address = {Birmingham, UK},
author = {Sebastian Raschka, and Yuxi (Hayden) Liu, and Vahid Mirjalili},
isbn = {978-1801819312},
publisher = {Packt Publishing},
title = {{Machine Learning with PyTorch and Scikit-Learn}},
year = {2022}
}
Please see the ch01/README.md file for setup recommendations.