Learn how to build a stock web application with Python.
edureka! provides a free tutorial on how to get started with Machine Learning in R.
edureka! provides a basic introduction to data science in this livestream from earlier today.
In this video, Mandy from deeplizard demonstrate how to fine-tune a pre-trained model called VGG16 to classify images as cats and dogs.
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.
- 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
OpenAI’s GPT-3 is quite the feat of AI engineering and now we have Two Minute Papers’ take on it.
Two Minute Papers explains the paper “Local Motion Phases for Learning Multi-Contact Character Movements” in the video below.
GPT-3 has 175 billion parameters/synapses and produces astonishing results.
Human brain has 100 trillion synapses. How much will it cost to train a language model the size of the human brain?
Lex Fridman works through the math.
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.
Two Minute Papers explores the paper “StarGAN v2: Diverse Image Synthesis for Multiple Domains”