⚡ A Fast, Extensible Progress Bar for Python and CLI

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.

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

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

Open standard for machine learning interoperability

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

一款入门级的人脸、视频、文字检测以及识别的项目.

Keras implementations of Generative Adversarial Networks.

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

AutoML library for deep learning

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

A Deep-Learning-Based Chinese Speech Recognition System 基于深度学习的中文语音识别系统

Setup and customize deep learning environment in seconds.

MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

Deep Reinforcement Learning for Keras.

Keras model to generate HTML code from hand-drawn website mockups. Implements an image captioning architecture to drawn source images.

Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Model summary in PyTorch similar to `model.summary()` in Keras