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Python Data Science Handbook: full text in Jupyter Notebooks
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
🔊 Text-Prompted Generative Audio Model
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.
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 instead.
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
This repository contains the Hugging Face Agents Course.
The "Python Machine Learning (1st edition)" book code repository and info resource
Public facing notes page
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
QLoRA: Efficient Finetuning of Quantized LLMs
Automatic extraction of relevant features from time series:
Image restoration with neural networks but without learning.
Notebooks and code for the book "Introduction to Machine Learning with Python"
Recipes for using Python's pandas library
Code for Tensorflow Machine Learning Cookbook
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
COCO API - Dataset @ http://cocodataset.org/
Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Code samples from the book Web Scraping with Python http://shop.oreilly.com/product/0636920034391.do
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Python code for common Machine Learning Algorithms
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Text and supporting code for Think Stats, 2nd Edition
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
Useful functions, tutorials, and other Python-related things