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100-days-of-ml-code

100 Days of ML Coding

aind-planning

aind-sudoku

artificial-intelligence

autokeras

Accessible AutoML for deep learning.

autophrase

AutoPhrase: Automated Phrase Mining from Massive Text Corpora

biograkn

BioGrakn Knowledge Graph

carnd-term1-starter-kit

carnd-transfer-learning-lab

decanlp

The Natural Language Decathlon: A Multitask Challenge for NLP

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

deep4cast

Probabilistic Multivariate Time Series Forecast using Deep Learning

direct_capstone

Repository for the direct capstone project. Application of molecular data science for studying phthalate plasticizers

dqn-tensorflow

Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning

eigensheep

massively parallel experimentation with Jupyter and AWS Lambda 🐑🌩📒

exchangelib

Python client for Microsoft Exchange Web Services (EWS)

genomic-ulmfit

ULMFiT for Genomic Sequence Data

gnes

GNES is Generic Neural Elastic Search, a cloud-native semantic search system based on deep neural network.

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

graph-data-science

Source code for the Neo4j Graph Data Science library of graph algorithms.

highs

Linear optimization software

how-to-learn-from-little-data

This is the code for "How to Learn from Little Data - Intro to Deep Learning #17' by Siraj Raval on YouTube

iapws

python libray for IAPWS standard calculation of water and steam properties

kdd18-gluon

KDD18 Tutorial: Deep Learning and Natural Language Processing with Apache MXNet (Incubating) Gluon

kedro

A Python library that implements software engineering best-practice for data and ML pipelines.

machine-learning

Content for Udacity's Machine Learning curriculum

natural-language-processing

Resources for "Natural Language Processing" Coursera course.

or-tools

Google's Operations Research tools

piconnect

A python connector to the OSISoft PI and PI-AF databases

poincare-embeddings

PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"