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Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
A colab gradio web UI for running Large Language Models
Code and data accompanying Natural Language Processing with PyTorch published by O'Reilly Media https://amzn.to/3JUgR2L
Official implementation for "Automatic Chain of Thought Prompting in Large Language Models" (stay tuned & more will be updated)
Simple UI for LLM Model Finetuning
I took Andrew Ng's Machine Learning course on Coursera and did the homework assigments... but, on my own in python because I love jupyter notebooks!
Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
Code, Notebooks and Examples from Practical Business Python
Udemy üzerindeki Python kurslarında kullanılan materyaller
Approaching (Almost) Any Machine Learning Problem中译版，在线文档地址：https://ytzfhqs.github.io/AAAMLP-CN/
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Use `llama2-wrapper` as your local llama2 backend for Generative Agents/Apps.
Python code for YouTube videos.
DeepStream SDK Python bindings and sample applications
Gallery of OSMnx tutorials, usage examples, and feature demonstrations.
主要存储Datawhale组队学习中“数据挖掘/机器学习”方向的资料。
A comprehensive guide to building RAG-based LLM applications for production.
The repository host gives no description.
Samples using the Agent2Agent (A2A) Protocol
Collection of notebook guides created by the Brev.dev team!
Awesome-pytorch-list 翻译工作进行中......
A comprehensive guide to understanding and implementing large language models with hands-on examples using LangChain for GenAI applications.
This course is designed to guide beginners through the exciting world of Edge AI, covering fundamental concepts, popular models, inference techniques, device-specific applications, model optimization, and the development of intelligent Edge AI agents.