GitHub - SonyCSL/CSLAIERYou signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
GitHub - dgschwend/zynqnet: Master Thesis "ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network"
Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. Many applications demand for embedded solutions that integrate into existing systems with tight real-time and power constraints. Convolutional Neural Networks (CNNs) presently achieve record-breaking accuracies in all image understanding benchmarks, but have a very hig
GitHub - PaddlePaddle/Paddle: PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
English | 简体中文 | 日本語 Welcome to the PaddlePaddle GitHub. PaddlePaddle, as the first independent R&D deep learning platform in China, has been officially open-sourced to professional communities since 2016. It is an industrial platform with advanced technologies and rich features that cover core deep learning frameworks, basic model libraries, end-to-end development kits, tools & components as well
GitHub - gokceneraslan/awesome-deepbio: A curated list of awesome deep learning applications in the field of computational biology
A curated list of awesome deep learning applications in the field of computational biology 2007-08 | Fast model-based protein homology detection without alignment | Sepp Hochreiter, Martin Heusel, and Klaus Obermayer | Bioinformatics 2012-07 | Deep architectures for protein contact map prediction | Pietro Di Lena, Ken Nagata and Pierre Baldi Bioinformatics 2012-10 | Predicting protein residue–resi
GitHub - MickyDowns/deep-theano-rnn-lstm-car: Pack of Drones: Layered reinforcement learning (Q-learning w/ RNN) for complex "hunt" behaviors
Drone movement and coordination are learned thru five independently-trained neural networks in four categories of operation. Specifically: avoid: The first two neural networks enable the drone to avoid obstacles. The turn RNN trains a drone moving at constant speed to avoid stationary and moving obstacles. Inputs are a set of five sonar sensor readings that emanate from the front of the drone. Out
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GitHub - dennybritz/cnn-text-classification-tf: Convolutional Neural Network for Text Classification in Tensorflow
