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ae_ts

Auto encoder for time series

agile_data_code_2

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

albumentations

fast image augmentation library and easy to use wrapper around other libraries

ampdlib

Python implementation of the Automatic Multiscale Peak Detection (AMPD) by Felix Scholkmann et al., 2012

arrhythmia-nn

Neural network for detecting cardiac dysrhythmia

atm

Auto Tune Models - A multi-user, multi-data system for model selection and tuning.

autokeras

accessible AutoML for deep learning.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

awesome_tensorflow_implementations

awesome_time_series_in_python

This curated list contains python packages for time series analysis

bnn-pynq

building-recommendation-engines

Building Recommendation Engines by Packt

camera_identification

IEEE's Signal Processing Society - Camera Model Identification

capsnet-keras

A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error < 0.4%.

capsnet-tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

challenge-data

challenge_cnn_tf

chess-alpha-zero

Chess reinforcement learning by AlphaGo Zero methods.

ciftify

The tools of the Human Connectome Project (HCP) adapted for working with non-HCP datasets

clnn

OpenCL backend for Torch nn neural networks library

cnn_tsc

A CNN for time-series classification

co2_forecast

Example of forecasting with a seasonal ARIMA model using the Mauna Loa Observatory CO2 dataset

coloring-greyscale-images-in-keras

Coloring B&W portraits with neural networks.

courses

fast.ai Courses

data-science--cheat-sheet

Cheat Sheets

data-science-from-scratch

code for Data Science From Scratch book

data-science-interview-questions-and-answers

Data science interview questions with answers. Not ideally (yet)

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.