Directory

Search results

Published directory entries matching your search.

Search results

103 listings
github.com

Deep learning based facial detector for Python coming with facial landmarks.

Models & Machine Learning 0
singa.apache.org

Apache SINGA is an Apache Top Level Project focused on distributed training of deep learning and machine learning models. It supports data parallel training across multiple GPUs on a single node or across different nodes, automatic gradient calculation, and loading and saving ONNX format models. Models trained with SINGA can be queried as stored procedures in an RDBMS.

Models & Machine Learning 0

An index page from LabLab for discovering AI technologies and related topics. Each technology page is described as offering tutorials, upcoming hackathons, frameworks and boilerplates. Listed technologies include GPT-4, ChatGPT, BERT, AI21 Labs, Anthropic, AWS, Cohere, Hugging Face and DeepSeek.

Models & Machine Learning 0
github.com

TensorLayer is an open-source deep learning and reinforcement learning library designed for scientists and engineers. Hosted on GitHub, the project offers design feature documentation, multilingual resources, and extensive example materials to support use.

Models & Machine Learning 0
github.com

TNN is a deep learning inference framework for mobile, desktop and server, developed by Tencent Youtu Lab and Guangying Lab. It is based on ncnn and Rapidnet and extends support to X86 and NV GPUs. The project states that it has been used in Tencent applications including Mobile QQ, Weishi and Pitu.

Models & Machine Learning 0
vast.ai

Vast.ai is a cloud GPU rental platform for AI, machine learning, deep learning and rendering workloads. Prices are set by supply and demand across 40+ data centers, with on-demand, interruptible and reserved instance types and per-second billing. Deployment is available through console, CLI, SDK and REST API.

Models & Machine Learning 0
webmachinelearning.github.io

WebNN is a web standard that lets web apps and frameworks accelerate deep neural networks using on-device hardware such as GPUs, CPUs, or purpose-built AI accelerators. The introduction states that in-browser inference supports local media sources, keeps user data on-device, and needs no network after initial asset caching.

Models & Machine Learning 0