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DeepPavlov is an open-source library for deep learning end-to-end dialog systems and chatbots. Version 1.0 is described as an NLP framework built on PyTorch and transformers for modular, configuration-driven development of NLP models, intended for practitioners with limited NLP/ML knowledge. It supports Linux, Windows 10+ through WSL/WSL2, and MacOS Big Sur+ on Python 3.6 to 3.10.
DeepSeek is a GitHub organization with 44 repositories. Its public projects include DeepEP, an expert-parallel communication library, FlashMLA, efficient multi-head latent attention kernels, and DeepGEMM, a BLAS kernel library on GPU. Several repositories are TypeScript, Cuda or C++ projects released under the MIT license.
Deep learning optimization library that makes distributed training easy, efficient, and effective.
Deep learning in Rust, with shape checked tensors and neural networks.
A deep learning-based translation library between 50 languages, built with transformers.
dpnn is a package of deep extensions to the nn neural network library, providing extra modules and criterions beyond the main nn package. It includes modules such as Serial, Inception, ReverseTable, SpatialUniformCrop and SpatialGlimpse, plus modules and criterions for implementing the REINFORCE algorithm.
Deep learning operations reinvented (for pytorch, tensorflow, jax and others).
FATE (Federated AI Technology Enabler) is an industrial grade federated learning open source framework that enables enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation, and provides federated learning algorithms including logistic regression, tree-based algorithms, deep learning and transfer learning.
fenn is a Python framework for building machine learning and deep learning workflows and LLM agents. It provides prebuilt trainers, agent templates, logging, and configuration management, with YAML configuration parsed and injected into the entrypoint with CLI override support. It is released under the MIT license.
Library for high performance deep learning inference on NVIDIA GPUs.
Gold is a reinforcement learning library for Go. It provides a set of agents for solving challenges in various environments, with composable tooling for creating agents and visualizing their performance. Agents include Q Learning, Deep Q learning with Double Q, REINFORCE, Natural Evolution Strategies, Hindsight Experience Replay and Proximal Policy Optimization, which is marked in progress.
GPT-NeoX is EleutherAI's library for training large-scale language models on GPUs. It builds on NVIDIA's Megatron and adds DeepSpeed techniques plus optimizations. It supports launching via Slurm, MPI and the IBM Job Step Manager, and offers predefined configurations for architectures including Pythia, PaLM, Falcon and LLaMA 1 & 2. The repository states it is likely the wrong library for those not training models with billions of parameters from scratch.
The official GPT4Free repository, a collection of various powerful language models hosted on GitHub. The repository includes access to models such as opus 4.6, gpt 5.3, kimi 2.5, deepseek v3.2, and gemini 3, and accepts community contributions.
Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
I am trask is a machine learning craftsmanship blog. Posts listed include Decentralized AI in 50 Lines of Python, Is the Universe Random?, Safe Crime Detection, Deep Learning without Backpropagation and Building Safe A.I.
Keras is a deep learning API that focuses on debugging speed, code conciseness and maintainability. It uses a multi-backend approach that allows work with JAX, TensorFlow and PyTorch, and the KerasHub library provides Keras 3 implementations of popular model architectures with pretrained checkpoints.
Keras-contrib is the official extension repository for the Keras deep learning library, with additional layers, activations, loss functions and optimizers not yet in Keras. It is deprecated and archived; the project migrated to TensorFlow Addons. The repository is read-only.
Koç University Deep Learning Framework.
KotlinDL is a high-level deep learning API written in Kotlin and inspired by Keras. It uses TensorFlow Java API and ONNX Runtime API for Java, and offers APIs for training models from scratch, importing Keras and ONNX models for inference, and transfer learning.
This webpage provides a curated list of large language model examples, alongside insights on business adoption, language model training, and influential models. It includes comparative analysis of leading LLMs such as OpenAI’s GPT-5, Claude 4.6, Gemini, DeepSeek-R1, Qwen, Llama 4, and xAI Grok-4 and Grok-4.1.
LKYDeepNN is a header-only deep neural network library for embedded systems. It has low dependencies, relying solely on the C++11 standard template library, and offers good portability. The project is hosted as a public repository on GitHub.
This is a blog post from Confident AI that serves as a guide to LLM evaluation metrics. It covers retrieval-augmented generation (RAG) metrics such as faithfulness and answer relevancy, agent metrics, LLM-as-a-judge approaches, and includes working DeepEval code examples.
LLM Stats is an AI leaderboard that ranks GPT, Claude, Gemini, Llama, DeepSeek and more than 300 AI models by intelligence, speed and price. Scores are computed from public benchmarks and live API metrics.
Machine Learning Mastery is a website that provides educational content such as tutorials, quick start guides, and eBooks to help developers learn and apply machine learning concepts and algorithms. It covers topics like deep learning, computer vision, natural language processing, and specific libraries including TensorFlow, PyTorch, and scikit-learn.
