Deep Learning

Data augmentation libarary for Deep Learning, which supports images, segmentation masks, labels and keypoints. Furthermore, SOLT is fast and has OpenCV in its backend.

A paper list of object detection using deep learning. I worte with reference to this survey paper

This is the implementation of paper Deep Exemplar-based Colorization by Mingming He*, Dongdong Chen*, Jing Liao, Pedro V.

The ONNX Model Zoo is a collection of pre-trained models for state-of-the-art models in deep learning, available in the ONNX format.

ktext performs common pre-processing steps associated with deep learning (cleaning, tokenization, padding, truncation).

Parallax is a tool that automatically parallelizes training of a single-GPU deep learning model correctly and efficiently in distributed multi-GPU environments.

These lessons, developed during the course of several years while I've been teaching at Purdue and NYU, are here proposed for the Computational and Data Science for High Energy Physics (CoDaS-HEP) summer school at Princeton University.

Finetune ships with a pre-trained language model from "Improving Language Understanding by Generative Pre-Training" and builds off the OpenAI/finetune-language-model repository.

Organized Resources for Deep Learning in Natural Language Processing.

Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

This project includes a Tensorflow implementation of DeepFM

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.