Showing 121-140 of 817 projects
PaddlePaddle Lite is a high-performance deep learning inference engine for mobile and edge devices.
Flax is a flexible neural network library for JAX, designed for easy experimentation and research in machine learning.
Solutions and notes for the Machine Learning Specialization by Andrew NG on Coursera.
CoreNet is a library for training deep neural networks, with a focus on building AI-powered coding tools.
Open-source PyTorch library for building neural machine translation and language models.
A PyTorch library for synthesizing and manipulating high-resolution 2048x1024 images using conditional GANs.
An open-source library for deep learning end-to-end dialog systems and chatbots.
Officially maintained deep learning models by PaddlePaddle, covering computer vision, NLP, speech, and more.
A collection of cheatsheets for the Stanford CS 230 Deep Learning course, covering key concepts and techniques.
An architecture-free neural network library for Node.js and the browser, useful for building AI-powered applications.
NYU Deep Learning Spring 2020 project for building and experimenting with deep learning models using PyTorch.
A curated list of papers and resources on neural radiance fields (NeRF), a powerful technique for 3D scene representation.
A deep learning library for image recognition tasks using residual neural networks.
A friendly machine learning library for the web, providing easy-to-use AI/ML capabilities for developers.
A tutorial for building Neural Machine Translation models using TensorFlow.
An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations.
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
This GitHub repository provides a comprehensive collection of TensorFlow 2.x tutorials and examples for AI/ML developers.
A flexible, high-performance serving system for deploying and managing machine learning models.
TensorFlow documentation repository with code examples and tutorials for deep learning and machine learning.
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