Evaluates AI performance using TensorFlow; tests speed, accuracy, power, and memory in deep learning on CPUs, GPUs, and TPUs.




Evaluates AI performance using TensorFlow; tests speed, accuracy, power, and memory in deep learning on CPUs, GPUs, and TPUs.




TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.
Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch.
A web application that you can use to train a machine learning model and make it recognize your images

Corpus2GPT: A project enabling users to train their own GPT models on diverse datasets, including local languages and various corpus types, using Keras and compatible with TensorFlow, PyTorch, or JAX backends for subsequent storage or sharing.

Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor.
Ray is an open-source unified compute framework that makes it easy to scale AI and Python workloads — from reinforcement learning to deep learning to tuning, and model serving. Learn more about Ray’s rich set of libraries and integrations.

AWS Neuron is the software development kit (SDK) used to run deep learning and generative AI workloads on AWS Inferentia- and AWS Trainium-powered Amazon Elastic Compute Cloud (Amazon EC2) instances. It includes a compiler, runtime, training and inference libraries, and...
A collection of infrastructure and tools for research in neural network interpretability.
