Name: Brian Eads
Type: User
Company: Bayer Crop Science
Bio: At Bayer my technical work is in data science, analytics engineering, high-performance and cloud computing, computational biology, and application development.
Location: St Louis MO
Brian Eads's Projects
A collection of research papers on decision, classification and regression trees with implementations.
A curated list of awesome deep learning applications in the field of computational biology
BayerCLAW workflow orchestration system for AWS
Config files for my GitHub profile.
A repository of 60 useful data science prompts for ChatGPT
The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Implementation of https://arxiv.org/pdf/1512.03385.pdf
Examples of using deep learning in Bioinformatics
For anyone who are eager to applying deep learning in bioinformatics!
GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.
Helsinki Neural Machine Translation system
Fit interpretable models. Explain blackbox machine learning.
Read IRMS (Isotope Ratio Mass Spectrometry) data files into R
:closed_book:machine learning tech collections at Microsoft and subsidiaries.
Create mathematical art with R
Convolutional Neural Network for 3D meshes in PyTorch
Moby Project - a collaborative project for the container ecosystem to assemble container-based systems
Distributed machine learning infrastructure for large-scale robotics research
Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Training and evaluating a variational autoencoder for pan-cancer gene expression data
Implementation of Very Deep Convolutional Neural Network for Text Classification