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aind-isolation

aind-nlp

Coding exercises for the Natural Language Processing concentration, part of Udacity's AIND program.

aind-nlp-bookworm

A simple question-answering system built using IBM Watson's NLP services.

aind-planning

aind-recognizer

aind-sudoku

aind2-nlp-capstone

aind2-rnn

Project materials for RNN segment of AIND nanodegree

awesome-deep-learning-papers

The most cited deep learning papers

capstone

chestviewsplit

Automatically split the chest x-ray into two views

clustering

Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

course-v3

The 3rd edition of course.fast.ai - coming in 2019

embeddings

Code for AMIA CRI 2016 paper "Learning Low-Dimensional Representations of Medical Concepts" (http://cs.nyu.edu/~dsontag/papers/ChoiChiuSontag_AMIA_CRI16.pdf)

epicolon

Computational pathology in colon cancer using the Epicolon cohort

fasttext

Library for fast text representation and classification.

gen

gen_drug

Predicting drug sensibility using mutations and gene expression

gensim

Topic Modelling for Humans

lung-segmentation-2d

Lung fields segmentation on CXR images using convolutional neural networks.

medlinker

ECIR 2020 - MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching

model_evaluation_exercise

Built a model to predict the value of a given house in the Boston real estate market using various statistical analysis tools. Identified the best price that a client can sell their house utilizing machine learning.

pharma_cv

python-docs-samples

Code samples used on cloud.google.com

reinforcement_learning

Applied reinforcement learning to build a simulated vehicle navigation agent.

rx-explorer

rx-thorax-automatic-captioning

stc2

Demo code for the paper STC2 which released three short text datasets for clustering and classification

supervised_models

Supervised ML models to predict student's performance in high school

tensorflow

Computation using data flow graphs for scalable machine learning