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beppinns

BEP of Jort Bouma TU Delft

big-data-code

Source code for Big Data: Principles and best practices of scalable realtime data systems

big-data-src

example source code for Big Data book (www.manning.com/marz)

big_data_analytics_web_text

For IU spring 2014 course, Big Data Analytics for Web and Text

bindsnet

Simulation of spiking neural networks (SNNs) using PyTorch.

binns

Biologically-informed neural networks

blackbox

Flight data recorder feature for the Naze32/Baseflight

blackbox-tools

Tools for working with blackbox flight logs

blocks

A Theano framework for building and training neural networks

blog-code

Code and files related to random side projects

bluefog

Distributed and decentralized training framework for PyTorch over graph

bonecv

Beaglebone Webcam and OpenCV Examples Repository

bonedevicetree

Working Project on the DeviceTree (note: files 3.8.11 Angstrom distro)

bookshelf

:books: books

brats17

Brain tumor segmentation for MICCAI 2017 BraTS challenge

cadence-detection

Scripts and experiments for cadence detection in Dutch folk songs

cadgms

calibrated_regression

A tutorial for the 2018 paper Accurate Uncertainties for Deep Learning Using Calibrated Regression by Kuleshov et al.

can-pinn

Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)

cancelout

CancelOut is a layer for deep neural networks, that can help identify a subset of relevant input features for streaming or static data.

carnd-capstone

cata-forget

cdh-twitter-example

Example application for analyzing Twitter data using CDH - Flume, Oozie, Hive

ceviche

:shrimp: Electromagnetic Simulation + Automatic Differentiation

chamferdistancepytorch

Chamfer Distance in Pytorch with f-score

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

characterizing-pinns-failure-modes

Characterizing possible failure modes in physics-informed neural networks.

chemotacticswarming

chemprop

Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.

ci-workshop-networks

Tutorials on network analysis, Discovery Engines Workshop at Computation Institute, Mar 18 2015