Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
There are 74,372 repositories under machine-learning topic.
An Open Source Machine Learning Framework for Everyone
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Monitor your servers, containers, and applications, in high-resolution and in real-time!
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
List of Computer Science courses with video lectures.
Deep Learning for humans
scikit-learn: machine learning in Python
Tesseract Open Source OCR Engine (main repository)
The world's simplest facial recognition api for Python and the command line
The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
DeepFaceLab is the leading software for creating deepfakes.
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
100 Days of ML Coding
🧑🏫 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, ... 🧠
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
AI-Powered Photos App for the Decentralized Web 🌈💎✨
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Roadmap to becoming an Artificial Intelligence Expert in 2022
A complete daily plan for studying to become a machine learning engineer.
Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.
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, AWS, and various command lines.
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow