Popular GitHub Repositories for Keras Js

Visualizer for neural network, deep learning and machine learning models

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

?Vite+Vue3+Gin拥有AI辅助的基础开发平台，支持TS和JS混用。它集成了JWT鉴权、权限管理、动态路由、显隐可控组件、分页封装、多点登录拦截、资源权限、上传下载、代码生成器、表单生成器和可配置的导入导出等开发必备功能。

ncnn is a high-performance neural network inference framework optimized for the mobile platform

? A ranked list of awesome machine learning Python libraries. Updated weekly.

Open standard for machine learning interoperability

JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js

A neural network that transforms a design mock-up into a static website.

Essential Cheat Sheets for deep learning and machine learning researchers https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.

人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

一款入门级的人脸、视频、文字检测以及识别的项目.

The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.

? An exciting game of programming and Artificial Intelligence

Techniques for deep learning with satellite & aerial imagery

AutoML library for deep learning

Keras implementations of Generative Adversarial Networks.

Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)