Yannic Kilcher explains the paper “Hopfield Networks is All You Need.”

Hopfield Networks are one of the classic models of biological memory networks. This paper generalizes modern Hopfield Networks to continuous states and shows that the corresponding update rule is equal to the attention mechanism used in modern Transformers. It further analyzes a pre-trained BERT model through the lens of Hopfield Networks and uses a Hopfield Attention Layer to perform Immune Repertoire Classification.

Content outline:

  • 0:00 – Intro & Overview
  • 1:35 – Binary Hopfield Networks
  • 5:55 – Continuous Hopfield Networks
  • 8:15 – Update Rules & Energy Functions
  • 13:30 – Connection to Transformers
  • 14:35 – Hopfield Attention Layers
  • 26:45 – Theoretical Analysis
  • 48:10 – Investigating BERT
  • 1:02:30 – Immune Repertoire Classification

Solving a data science problem is about more than making a model.

It entails data cleaning, exploration, modeling and tuning, production deployment, and workflows governing each of these steps.

Databricks has a great video on how MLflow fits into the data science process.

In this simple example, we’ll take a look at how health data can be used to predict life expectancy. Starting with data engineering in Apache Spark, data exploration, model tuning and logging with hyperopt and MLflow. It will continue with examples of how the model registry governs model promotion, and simple deployment to production with MLflow as a job or dashboard.