Jenkins From Scratch.

ESTO ES LEAN, Resolviendo la paradoja de la Eficiencia

Approaching (Almost) Any Machine Learning Problem

Libro: https://github.com/abhishekkrthakur/app ... AAAMLP.pdf

An Introduction to Statistical Learning

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

The book has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian and Vietnamese.

The First Edition topics include:
- Sparse methods for classification and regression
- Decision trees
- Boosting
- Support vector machines
- Clustering

The Second Edition adds:
- Deep learning
- Survival analysis
- Multiple testing
- Naive Bayes and generalized linear models
- Bayesian additive regression trees
- Matrix completion

Libro: https://www.statlearning.com/

Understanding Deep Learning.

Link del Libro: https://udlbook.github.io/udlbook/

Table of contents

Chapter 1 - Introduction
Chapter 2 - Supervised learning
Chapter 3 - Shallow neural networks
Chapter 4 - Deep neural networks
Chapter 5 - Loss functions
Chapter 6 - Training models
Chapter 7 - Gradients and initialization
Chapter 8 - Measuring performance
Chapter 9 - Regularization
Chapter 10 - Convolutional networks
Chapter 11 - Residual networks
Chapter 12 - Transformers
Chapter 13 - Graph neural networks
Chapter 14 - Unsupervised learning
Chapter 15 - Generative adversarial networks
Chapter 16 - Normalizing flows
Chapter 17 - Variational auto-encoders
Chapter 18 - Diffusion models
Chapter 19 - Deep reinforcement learning
Chapter 20 - Why does deep learning work?

Hands on Data Structures and Algorithms with Python.

The Python Workbook. A Brief Introduction with Exercises and Solutions.

Advanced Guide to Python 3 Programming

Python for Data Science, Build Your Python Data Science Skills.

GitHub & GitLab CI/CD.