Pinned Repositories
BNN
Bayesian Neural Networks
cui2vec
deep_learning_works
Code to accompany "You can probably use deep learning even if your data isn't that big" blog post
deeplearning_101
Disease-Ontology-Tools
HMC_GPU
Code to accompany the paper "Fast Hamiltonian Monte Carlo Using GPU Computing"
language-model
medical-data
Medical-Embeddings-Benchmark
mimic3-nicu-benchmarks
Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database.
beamandrew's Repositories
beamandrew/medical-data
beamandrew/deep_learning_works
Code to accompany "You can probably use deep learning even if your data isn't that big" blog post
beamandrew/cui2vec
beamandrew/BNN
Bayesian Neural Networks
beamandrew/HMC_GPU
Code to accompany the paper "Fast Hamiltonian Monte Carlo Using GPU Computing"
beamandrew/deeplearning_101
beamandrew/language-model
beamandrew/Disease-Ontology-Tools
beamandrew/Medical-Embeddings-Benchmark
beamandrew/mimic3-nicu-benchmarks
Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database.
beamandrew/patient2vec
beamandrew/BMI-703
Notebooks for BMI 703 Guest Lecture on Deep Learning
beamandrew/CheXNet-Keras
This project is a tool to build CheXNet-like models, written in Keras.
beamandrew/cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
beamandrew/HSPH_lecture
Lecture given at HSPH on 10/25/2017
beamandrew/lasic
beamandrew/Spearmint
Spearmint Bayesian optimization codebase
beamandrew/wdnn
beamandrew/data_science_screen
beamandrew/embeddings_benchmark_paper
beamandrew/hpstr-jekyll-theme
A Jekyll theme with some tumble-log tendencies.
beamandrew/jQuery-EasyTabs
Easy and flexible jQuery tabbed functionality without all the styling.
beamandrew/kristynbeam.github.io
beamandrew/lm
beamandrew/medscape_parser
beamandrew/mne-python
MNE : Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python
beamandrew/pytorch-utils
Some simple helper functions for working with pytorch