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A complete computer science study plan to become a software engineer.
An Open Source Machine Learning Framework for Everyone
Master the command line, in one page
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
A curated list of awesome Machine Learning frameworks, libraries and software.
⭐️ Companies that don't have a broken hiring process
Python Data Science Handbook: full text in Jupyter Notebooks
A list of semi to fully remote-friendly companies (jobs) in tech.
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials,…
Integrate cutting-edge LLM technology quickly and easily into your apps
Awesome-LLM: a curated list of Large Language Model
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
The official Python SDK for Model Context Protocol servers and clients
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
A WebGL accelerated JavaScript library for training and deploying ML models.
VIP cheatsheets for Stanford's CS 229 Machine Learning
👔 💼 Build fast 🚀 and easy multiple beautiful resumes and create your best CV ever! Made with Vue and LESS.
A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
📚 Community guides for open source creators
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
📋 A list of open LLMs available for commercial use.
Lime: Explaining the predictions of any machine learning classifier