Deep Learning for humans
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Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
pix2tex: Using a ViT to convert images of equations into LaTeX code.
Convert Machine Learning Code Between Frameworks
Low-code framework for building custom LLMs, neural networks, and other AI models
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Fast and Accurate ML in 3 Lines of Code
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Aim 💫 — An easy-to-use & supercharged open-source experiment tracker.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
ZenML 🙏: One AI Platform from Pipelines to Agents. https://zenml.io.
Scalable and user friendly neural :brain: forecasting algorithms.
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.
Don't memorize ML. Understand it from first principles including Inference, Cuda, Deep Learning, Data Science and Reinforcement Learning
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
Engine for AI/ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.
Code for PaperRobot: Incremental Draft Generation of Scientific Ideas
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
Deep and Machine Learning for Microscopy
MCP server for spatial transcriptomics analysis through natural language interfaces.