Lists (2)
Stars
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.
算法岗笔试面试大全，励志做算法届的《五年高考，三年模拟》！
Open-source, accurate and easy-to-use video speech recognition & clipping tool, LLM based AI clipping intergrated.
real time face swap and one-click video deepfake with only a single image
airockchip / yolov7
Forked from WongKinYiu/yolov7Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
NEW - YOLOv8 🚀 in PyTorch > ONNX > CoreML > TFLite
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
[NeurIPS 2024] Maximum Entropy Reinforcement Learning via Energy-Based Normalizing Flow
On-device wake word detection powered by deep learning
Experiments for the Neural Autoregressive Flows paper
一个收集C/C++新手学习的入门项目，整理收纳开发者开源的小项目、工具、框架、游戏等，视频，书籍，面试题/算法题，技术文章。
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，如 fiddler、mitmproxy，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，…
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/
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Curated list of project-based tutorials
freeCodeCamp.org's open-source codebase and curriculum. Learn math, programming, and computer science for free.
Official Pytorch Implementation of CMLO in the paper ”When to Update Your Model: Constrained Model-based Reinforcement Learning“
A pytorch reprelication of the model-based reinforcement learning algorithm MBPO
Code for the paper "When to Trust Your Model: Model-Based Policy Optimization"
Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%
Code for the paper "Generative Adversarial Imitation Learning"