Python Data Science Handbook: full text in Jupyter Notebooks
- scikit-learn
- numpy
- python
- jupyter-notebook
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Python Data Science Handbook: full text in Jupyter Notebooks
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
The fastai book, published as Jupyter Notebooks
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
The "Python Machine Learning (1st edition)" book code repository and info resource
An open access book on scientific visualization using python and matplotlib
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`
code for Data Science From Scratch book
🥞Data Structures and Algorithms explained and implemented in JavaScript + eBook
TranslatedBook_3_"Elements of Mathematics" | Iris Book series: from addition, subtraction, multiplication and division to machine learning; now on sale; corrections are still welcome, and readers who submit many corrections will also receive free books!
Become a cracked AI/ML researcher/engineer with this unconventional textbook covering maths, computing, and ML with intuition.
The "Python Machine Learning (2nd edition)" book code repository and info resource
Pro Git 2nd Edition
Code Repository for Machine Learning with PyTorch and Scikit-Learn
R for data science: a book
A computer science textbook
The "Python Machine Learning (3rd edition)" book code repository
It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Text and supporting code for Think Stats, 2nd Edition