Machine Learning
Showing 36 of 645 projects
The fastai book, published as Jupyter Notebooks, provides an introduction to deep learning, fastai, and PyTorch.
Generates bitmaps and tilemaps that are locally similar to a single input example, using a constraint-solving algorithm inspired by quantum mechanics.
Python implementations of popular machine learning algorithms from scratch with interactive Jupyter demos and mathematical explanations.
An industrial deep learning framework supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
An industrial deep learning framework from China supporting unified dynamic/static graphs, automatic parallelism, and integrated training/inference for large models.
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
A curated list of awesome computer vision resources, including papers, datasets, software, and courses.
A curated list of awesome computer vision resources, including papers, datasets, software, and tutorials.
A community-driven repository tracking datasets and state-of-the-art results for common NLP tasks across multiple languages.
A lightweight Python library for face recognition and facial attribute analysis (age, gender, emotion, race) with a unified API.
A fast, memory-efficient reimplementation of OpenAI's Whisper speech-to-text model using CTranslate2.
A production-grade Rust-native trading engine with deterministic event-driven architecture for multi-asset, multi-venue systems.
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
Fast automatic speech recognition with accurate word-level timestamps and speaker diarization, built on OpenAI's Whisper.
Open source machine learning framework for building contextual text- and voice-based chatbots and assistants.
A free, self-taught curriculum following undergraduate Data Science guidelines using MOOCs from top universities.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
A flexible and efficient deep learning framework that mixes symbolic and imperative programming for heterogeneous distributed systems.
An open standard format for representing machine learning models to enable interoperability between frameworks.
A reactive Python notebook that's reproducible, git-friendly, and deployable as scripts or apps.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
A curated list of awesome open-source libraries for deploying, monitoring, versioning, and scaling production machine learning systems.
An automatic forecasting procedure for time series data with multiple seasonality and linear or non-linear growth.
A minimalist, high-performance machine learning framework for Rust with a focus on serverless inference and GPU support.
A cross-platform, high-performance accelerator for machine learning inference and training with ONNX models.
A research project exploring machine learning for generating music, images, and art using deep learning and reinforcement learning.
An open-source framework for financial large language models, enabling cost-effective fine-tuning for tasks like sentiment analysis and forecasting.
Comprehensive cheatsheets and refreshers covering all key concepts from Stanford's CS 229 Machine Learning course.
A hardware-accelerated JavaScript library for training and deploying machine learning models in browsers and Node.js.
A hardware-accelerated JavaScript library for training and deploying machine learning models in the browser and Node.js.
A decentralized graph database and synchronization protocol for building real-time, offline-first applications with end-to-end encryption.
An open-source platform for debugging, evaluating, and monitoring LLM applications, RAG systems, and agentic workflows.
A Python framework for computing and training state-of-the-art text embeddings, rerankers, and sparse encoders.
A curated list of resources dedicated to Natural Language Processing (NLP), including libraries, datasets, tutorials, and research.
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