suraj-deshmukh

get_tweets

awesome-deep-vision

A curated list of deep learning resources for computer vision

categorical-encoding

A library of sklearn compatible categorical variable encoders

connexion

Swagger/OpenAPI First framework for Python on top of Flask with automatic endpoint validation and OAuth2 support

convnets-keras

deep-learning-keras-euroscipy2016

# Deep Learning with Keras

deep-learning-models

Keras code and weights files for popular deep learning models.

dist-keras

Distributed deep learning with Keras and Apache Spark.

dl-models-for-qa

Keras DL models to answer 8th grade science multiple choice questions (Kaggle AllenAI competition).

doc-classify

Document classification.

FaceRecog-Keras

Face Recognition using Neural Networks implemented using keras library

headlines

Automatically generate headlines to short articles

imbalanced-learn

Python module to perform under sampling and over sampling with various techniques.

jobdescription2jobtitle

classify a job description (or noisy job title) into a ONET job title

keras-cam

Keras implementation of class activation mapping

keras-js

Run trained Keras models in the browser, with GPU support

kerasR

R interface to the keras library

lectures

Oxford Deep NLP 2017 course

machine-learning

Web-interface, and programmatic API for classification and regression predictions

music-auto_tagging-keras

Music auto-tagging models and trained weights in keras/theano

Neural-Style-Transfer

Implementation of Neural Style Transfer from the paper [A Neural Algorithm of Artistic Style](http://arxiv.org/abs/1508.06576) in Keras 1.0.6

Object-detection-with-deep-learning-and-sliding-window

Introduces an approach for object detection in an image with sliding window. The repository contains three files, make_data.py reads the image in gray scale and converts the image into a numpy array. The labels are also appended based on the file name. In this case, if the file name starts with "trn", then 1 is appended else 0. Finally, all the images and labels are saved into .npy file. The test-model-1.py file loads the images and converts the labels into two categories as we are doing binary classification of images. The model is built using keras with theano as backend. In this case, the best training accuracy was 80% since the data was just 500 images and the testing accuracy was 67%

question-answering

Question Answering System.

quiver

Interactive convnet features visualization for Keras

scikit-multilearn

A scikit-learn based module for multi-label et. al. classification

sklearn2pmml

Python library for converting Scikit-Learn pipelines to PMML

spark-sklearn

Scikit-learn integration package for Spark

talkbox

TensorFlow-Tutorials

Simple tutorials using Google's TensorFlow Framework

VQA_LSTM_CNN

Train a deeper LSTM and normalized CNN Visual Question Answering model. This current code can get 58.16 on OpenEnded and 63.09 on Multiple-Choice on test-standard.