A collection of deep learning architectures and applications ported to the python language and tools for basic medical image processing.
Here is my python source code for training an agent to play Tetris. It could be seen as a very basic example of Reinforcement Learning's application.
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
Classy Vision is a new end-to-end, PyTorch-based framework for large-scale training of state-of-the-art image and video classification models.
Deep Learning Framework for various Image Recognition Tasks. Photogrammetry and Robotics Lab, University of Bonn.
Alfred is command line tool for deep-learning usage. if you want split an video into image frames or combine frames into a single video, then alfred is what you want.
Larq is an open-source deep learning library for training neural networks with extremely low precision weights and activations, such as Binarized Neural Networks (BNNs).
Timeseries clustering is an unsupervised learning task aimed to partition unlabeled timeseries objects into homogenous groups/clusters.
This package sends your deep learning training loss and accuracy to your slack channel after every specified epoch. It uses slackclient and keras python packages.
DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation of deep learning optimizers.
Resemblyzer allows you to derive a high-level representation of a voice through a deep learning model (referred to as the voice encoder).
A PyTorch implementation of ESPCN based on CVPR 2016 paper "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network".
Quality medical information is valuable to everyone, but it's not always readily available. Doc Product aims to fix that.