Reuben Thomas
Deep learning now drives tools like Geneformer, Borzoi, ChromBPNet, and Akita—predicting gene expression, chromatin accessibility, and genome folding from sequence.
Join this two-session workshop that will give you an intuition-first grounding: when and why different architectures work, what data and compute they require, and how training and evaluation actually proceed—without heavy math.
Session one covers core concepts and architectures; session two examines specific biological models and includes simple worked examples. You’ll leave the workshop able to reason about which model fits a given biological problem, and why.
This is an advanced workshop in the Machine Learning series. No prior machine learning machine learning experience is needed.
