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Lawbda Co.
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Starred repositories
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Google Research
10 Weeks, 20 Lessons, Data Science for All!
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
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
A game theoretic approach to explain the output of any machine learning model.
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。
💿 Free software that works great, and also happens to be open-source Python.
A collection of various deep learning architectures, models, and tips
Scripts for fine-tuning Meta Llama with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supporting…
深度学习入门教程, 优秀文章, Deep Learning Tutorial
PRML algorithms implemented in Python
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
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
My blogs and code for machine learning. http://cnblogs.com/pinard
Visualizations for machine learning datasets
Recipes for using Python's pandas library
搜集、整理、发布 中文 自然语言处理 语料/数据集,与 有志之士 共同 促进 中文 自然语言处理 的 发展。
A scikit-learn compatible neural network library that wraps PyTorch
An annotated implementation of the Transformer paper.
Evidently is an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.
AirLLM 70B inference with single 4GB GPU
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.