Machine Learning
Showing 36 of 601 projects
A TensorFlow implementation of DeepMind's WaveNet neural network for generating raw audio waveforms.
A curated collection of papers, code, and resources for domain adaptation in machine learning.
DeepMind's library for building graph networks in TensorFlow and Sonnet, enabling graph-structured data processing with neural networks.
An open-source MLOps platform for building, orchestrating, and deploying production AI pipelines and agents.
Rust bindings for the C++ API of PyTorch, providing thin wrappers around libtorch.
An Emmy-winning perceptual video quality assessment algorithm that fuses multiple metrics to predict human visual quality.
An end-to-end framework for building custom AI applications and agents directly integrated with databases.
An open-source library for building massively scalable machine learning pipelines on Apache Spark.
A compiler that extends SQL with AI capabilities to train, predict, and evaluate machine learning models directly from SQL statements.
A suite of GPU-accelerated machine learning algorithms with scikit-learn compatible APIs for 10-50x faster performance on large datasets.
A Python library for image augmentation in machine learning, offering a stochastic pipeline approach with fine-grained control over operations.
A Python library providing extensions and utilities for data science and machine learning tasks.
Code repository for the 'Machine Learning with PyTorch and Scikit-Learn' book, providing practical examples and notebooks.
A high-performance Game Boy emulator written in Python, designed for AI training, game automation, and classic gameplay.
A hands-on beginner's guide to machine learning and image classification using Caffe and DIGITS with neural networks.
A curated list of awesome Generative Adversarial Network (GAN) applications and demonstrations across various domains.
A Python library implementing hybrid recommendation algorithms with metadata support for both implicit and explicit feedback.
A curated collection of links to conference publications, surveys, and software in graph-based deep learning.
Run trained Keras models directly in the browser with GPU acceleration via WebGL.
A curated collection of resources for building, training, serving, and optimizing production-grade Large Language Model applications.
A general-purpose tensor library for parallel computing across CPUs, GPUs, and hardware accelerators.
A machine learning library designed for human interpretability, featuring debuggable models and a feature transform language.
A curated collection of graph classification papers with implementations covering embeddings, deep learning, kernels, and factorization.
An engine-agnostic deep learning framework for Java developers, providing a high-level API for model training and inference.
A curated collection of graph classification papers with reference implementations covering embedding, deep learning, kernels, and factorization.
A Python library for lightning-fast univariate time series forecasting with optimized statistical and econometric models.
A Java client library for OpenAI's GPT APIs, including GPT-3, ChatGPT, and GPT-4.
A high-level library for training and evaluating neural networks in PyTorch with a flexible engine and event system.
An MLOps framework to package, deploy, monitor, and manage thousands of production machine learning models on Kubernetes.
A collection of infrastructure and tools for research in neural network interpretability and visualization.
A JAX implementation of OpenAI's Whisper model offering up to 70x faster transcription on TPUs.
A collection of samples demonstrating how to use ML.NET for various machine learning tasks in .NET applications.
Fast and simple OCR library for iOS/macOS using neural networks, optimized for short alphanumeric codes.
A fast and simple OCR library written in Swift, optimized for recognizing short alphanumeric codes like gift cards.
An open-source augmented analytics platform that automates exploratory data analysis and visualization with AI-powered insights.
A comprehensive Rust machine learning framework focused on preprocessing and classical algorithms, akin to scikit-learn.
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