#
Starred repositories
139
stars
written in Jupyter Notebook
Clear filter
A latent text-to-image diffusion model
18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Python Data Science Handbook: full text in Jupyter Notebooks
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Google Research
10 Weeks, 20 Lessons, Data Science for All!
⛔️ 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
100-Days-Of-ML-Code中文版
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
A collection of various deep learning architectures, models, and tips
📡 Simple and ready-to-use tutorials for TensorFlow
深度学习入门教程, 优秀文章, Deep Learning Tutorial
Natural Language Processing Tutorial for Deep Learning Researchers
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
This repository contains implementations and illustrative code to accompany DeepMind publications
PRML algorithms implemented in Python
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
YSDA course in Natural Language Processing
Interview = 简历指南 + 算法题 + 八股文 + 源码分析
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
Deep Learning Specialization by Andrew Ng on Coursera.
