DeepSpeed supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.
DeepSWE supports software development with code generation, analysis, debugging, or documentation.
Deep learning foundations and applications.
Multi-agent Deep-research System supports AI agents, automated workflows, orchestration, or delegated tasks.
r/DeepSeek points to a Reddit community or thread with discussion, updates, troubleshooting, or shared resources.
Synthetic Creativity - by Cavin - Deep Markets provides machine-learning models, research, training resources, or evaluation tools.
Paper by Dipankar Dasgupta, Deepak Venugopal and Kishor Datta Gupta.
Platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications.
List of awesome compiler projects and papers for tensor computation and deep learning.
Basic proof of concept for genetic architecture search in Keras.
Helps find, analyze or synthesize information for research and knowledge discovery.
Analyzes structured data and helps produce queries, insights or visualizations.
Supports software work with AI-assisted coding, testing or application development.
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.
Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis).
Theano based library for deep and recurrent neural networks.
Fast open framework for deep learning.
Torch-like deep learning framework for Javascript with support for tensors, autograd, optimizers, and other neural net constructs.
A PyTorch based deep learning library for drug pair scoring.
A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Course materials and notes for Stanford class CS231n: Deep Learning for Computer Vision.
This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING].