21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

12 Weeks, 24 Lessons, AI for All!

Data Engineering Zoomcamp is a free nine-week course that covers the fundamentals of data engineering.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

This is a repo with links to everything you'd ever want to learn about data engineering

Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama mode…

High-Resolution Image Synthesis with Latent Diffusion Models

强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Public facing notes page

Cosmos is a world model development platform that consists of world foundation models, tokenizers and video processing pipeline to accelerate the development of Physical AI at Robotics & AV labs. C…

本项目是一个面向小白开发者的大模型应用开发教程，在线阅读地址：https://datawhalechina.github.io/llm-universe/

A unified framework for 3D content generation.

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

Solve puzzles. Improve your pytorch.

All notes and materials for the CS229: Machine Learning course by Stanford University

Documentation for Google's Gen AI site - including the Gemini API and Gemma

统计学习方法习题解答，在线阅读地址：https://datawhalechina.github.io/statistical-learning-method-solutions-manual

Build large language model (LLM) apps with Python, ChatGPT and other models. This is the companion repository for the book on generative AI with LangChain.

[CVPR 2024] LMDrive: Closed-Loop End-to-End Driving with Large Language Models

Code accompanying the ECCV 2020 paper "Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data" by Tim Salzmann*, Boris Ivanovic*, Punarjay Chakravarty, and Marco Pavone (…

《动手学深度学习》习题解答，在线阅读地址如下：

State-of-the-art methods for human trajectory forecasting. Contains code for papers published at ECCV 2020 and ICCV 2021.

[ICLR'25] 3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation

Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”

This repository contains data readers and examples for the three tracks of the Shifts Dataset and the Shifts Challenge.

[ICCV'2023] Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders

PyTorch implementation of "Brain Decodes Deep Nets"