Python-Machine Learning

此存储库用于同步存储书籍--Machine Learning with PyTorch and Scikit-Learn相应章节的代码记录 。

This repository is used for synchronized storage of code records for the corresponding chapters of the book - Machine Learning with PyTorch and Scikit-Learn.

此存储库中的所有文件和代码脚本仅供学术用途。除此之外，请联系书籍作者Sebastian Raschka。

All files and code scripts in this repository are for academic use only. Beyond that, please contact the author of this book.

@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}  
}

---

Conda Enviroment Installation Instructions

# 建议单独创建虚拟环境, <newName>是将要创建的环境名称，例如'mlbook'.
conda create --name newName python=3.9

# 激活新建的虚拟环境.
activate newName

# 查看已有的虚拟环境，选择要切换到的虚拟环境.
conda info --envs
# 或者
conda env list

# 在命令行中切换到上述创建的newName虚拟环境.
conda activate newName

# 便于在jupyter中切换使用，在当前环境中安装ipykernel.
conda install ipykernel

python -m ipykernel install --name newName

# 安装第三方工具包,进入到requirements.txt所在路径.
pip install -r requirements-ch01.txt

---

Table of Contents and Jupyter Notebooks

1. Machine Learning - Giving Computers the Ability to Learn from Data.
2. Training Machine Learning Algorithms for Classification.
3. A Tour of Machine Learning Classifiers Using Scikit-Learn.
4. Building Good Training Sets – Data Pre-Processing.
5. Compressing Data via Dimensionality Reduction.
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization.
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 Multi-layer 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. [TODO]
16. Transformers -- Improving Natural Language Processing with Attention Mechanisms. [TODO]
17. Generative Adversarial Networks for Synthesizing New Data.
18. Graph Neural Networks for Capturing Dependencies in Graph Structured Data. [TODO]
19. Reinforcement Learning for Decision Making in Complex Environments. [TODO]