Weekly Machine Learning Opensource Roundup – July 12, 2018

Examples

Draw This.
A polaroid camera that draws cartoons. You point, and shoot – and out pops a cartoon; the camera’s best interpretation of what it saw.

word-mesh
A wordcloud/wordmesh generator that allows users to extract keywords from text, and create a simple and interpretable wordcloud, utilizing the various statistical, semantic and grammatical features of the text to inform visualization parameters.

RoboSat
Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water.

NeuralTitanic
View the predictions of a neural network while it is being trained on the titanic dataset.

Federalist Papers NLP
Attribution of the Federalist Papers through TF-IDF and ML

spoken language identification
Identify a spoken language using artificial intelligence (LID).

Fortnite – PUBG
Converting Fortnite texture to PUBG texture using Keras

Toolsets

lagom
A light PyTorch infrastructure to quickly prototype reinforcement learning algorithms.

PythonRobotics
A Python code collection of robotics algorithms, especially for autonomous navigation.

ElastiK Nearest Neighbors
Elasticsearch plugin for approximate K-nearest-neighbors on floating-point vectors

Models

Glow
Code for reproducing results in “Glow: Generative Flow with Invertible 1×1 Convolutions”

RelativisticGAN-Tensorflow
Simple Tensorflow implementation of RelativisticGAN

cond-shift-neurons
Implementation of Conditionally Shifted Neurons by Munkhdalai et al

DifferentiableNeuralComputer
Optimized Differentiable Neural Computer In Chainer

Switchable Normalization
A normalization technique that is able to learn different normalization operations for different normalization layers in a deep neural network in an end-to-end manner.

Libraries

TensorFlow on idris
Dependent types meets deep learning

jGeneticNeuralNet
A Java library that trains neural networks with a genetic algorithm.

Modin
Unify the way you interact with your data, a project derived from Parallelizing Pandas

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