JZhou's starred repositories
SelfDrivingElegooCar
Conditional imitation learning
End-to-end-Autonomous-Driving
All you need for End-to-end Autonomous Driving
torch-template-for-deep-learning
Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.
annotated_deep_learning_paper_implementations
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
evolutionary-model-merge
Official repository of Evolutionary Optimization of Model Merging Recipes
llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
CodeProject.AI-Server
CodeProject.AI Server is a self contained service that software developers can include in, and distribute with, their applications in order to augment their apps with the power of AI.
World-Models-Autonomous-Driving-Latest-Survey
A curated list of world models for autonomous driving. Keep updated.
Intelligent-Vehicle-Perception-Based-on-Inertial-Sensing-and-Artificial-Intelligence
Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence
Awesome-Radar-Perception
Radar Perception in Autonomous Driving
tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
leetcode-notes
🐳 LeetCode 算法笔记：面试、刷题、学算法。在线阅读地址：https://datawhalechina.github.io/leetcode-notes/
team-learning
主要展示Datawhale的组队学习计划。
EasyReinforcementLearning
EasyRL: An easy-to-use and comprehensive reinforcement learning package.
pyan
pyan is a Python module that performs static analysis of Python code to determine a call dependency graph between functions and methods. This is different from running the code and seeing which functions are called and how often; there are various tools that will generate a call graph in that way, usually using debugger or profiling trace hooks - for example: https://pycallgraph.readthedocs.org/ This code was originally written by Edmund Horner, and then modified by Juha Jeronen. See README for the original blog posts and links to their repositories.
visualblocks
Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.
OpenBBTerminal
Investment Research for Everyone, Everywhere.
Shared-Knowledge-Lifelong-Learning
[TMLR] Lightweight Learner for Shared Knowledge Lifelong Learning