Machine Learning
Showing 36 of 670 projects
A curated list of deep learning resources for computer vision, including papers, courses, books, and software.
A curated list of deep learning resources for computer vision, including papers, courses, books, and software.
A NumPy/SciPy-compatible array library for GPU-accelerated computing with Python, supporting NVIDIA CUDA and AMD ROCm.
A research framework for fast prototyping of reinforcement learning algorithms, designed for easy experimentation and reproducibility.
A Python framework for creating reproducible, maintainable, and modular data engineering and data science pipelines.
Fast, state-of-the-art tokenizers for training and tokenization, optimized for both research and production.
Upload a photo of any room to generate AI-powered redesigns and variations using the ControlNet ML model.
An open-source inference serving platform for deploying AI models from multiple frameworks across cloud, data center, and edge devices.
An elegant PyTorch-based deep reinforcement learning library with modular APIs for both research and application development.
A PyTorch implementation of YOLOv3 for real-time object detection, supporting export to ONNX, CoreML, and TFLite.
Code examples and tutorials for Stanford's TensorFlow for Deep Learning Research course (CS 20).
A practical booklet covering the four main steps of designing machine learning systems with 27 interview questions.
A framework for elegantly configuring complex applications, particularly in machine learning and research.
An automated machine learning library that trains and deploys high-accuracy models for tabular, text, image, and time series data with minimal code.
A curated repository of resources, datasets, and research papers for 3D machine learning, covering computer vision, graphics, and deep learning.
A comprehensive resource of deep learning techniques and models for analyzing satellite and aerial imagery.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A community-driven collection of data science interview questions and answers covering theory, technical skills, and probability.
A TensorFlow 2 library providing simple, composable abstractions for machine learning research via the snt.Module concept.
An open-source Python toolkit for speaker diarization with state-of-the-art pretrained models and pipelines.
A standardized, flexible project template for data science work using Cookiecutter to structure reproducible projects.
An open-source, low-code Python library that automates end-to-end machine learning workflows.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A unified Python framework for machine learning with time series, offering scikit-learn compatible tools for forecasting, classification, clustering, and more.
A curated list of reinforcement learning resources including theory, applications, code libraries, tutorials, and platforms.
A curated list of resources dedicated to reinforcement learning, including theory, applications, code, tutorials, and platforms.
A deep reinforcement learning library offering high-quality, single-file implementations of algorithms like PPO, DQN, and SAC for research and education.
A modular deep learning library providing a higher-level API for TensorFlow to speed up experimentation.
A Python library for flexible and readable tensor operations across numpy, PyTorch, JAX, TensorFlow, and other frameworks.
A batteries-included machine learning library for Go with a scikit-learn inspired interface.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
An open-source, cross-platform machine learning framework for .NET developers to build, train, and deploy custom ML models.
A comprehensive collection of machine learning and deep learning models, trading agents, and simulations for stock market forecasting.
An AutoML library for deep learning that automates model selection and hyperparameter tuning using Keras and TensorFlow.
A collection of beginner-friendly TensorFlow tutorials with accompanying YouTube videos covering deep learning fundamentals and advanced topics.
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