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TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
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.
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
100-Days-Of-ML-Code中文版
Learn OpenCV : C++ and Python Examples
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
📡 Simple and ready-to-use tutorials for TensorFlow
Your new Mentor for Data Science E-Learning.
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Tutorials, assignments, and competitions for MIT Deep Learning related courses.
Public facing notes page
Best Practices, code samples, and documentation for Computer Vision.
TensorFlow Tutorials with YouTube Videos
Image restoration with neural networks but without learning.
Notebooks and code for the book "Introduction to Machine Learning with Python"
Lab Materials for MIT 6.S191: Introduction to Deep Learning
The "Python Machine Learning (2nd edition)" book code repository and info resource
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Code for Tensorflow Machine Learning Cookbook
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
TensorFlow Basic Tutorial Labs
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.