Skip to content

Andrew Fairless, Ph.D.

Data, Science, and Tinkering

Overview
Experience and Education
Publications
SHAP Tutorial
Understanding the Basics of Bayesian Linear Regression
Classifying Medicine
The Peanuts Project

Search for:

Search for:

What I Read: Mamba Explained

Home/What I Learn/What I Read: Mamba Explained

By BylineAndrew Fairless on June 10, 2024April 15, 2024

https://thegradient.pub/mamba-explained

Mamba Explained
Kola Ayonrinde
27.Mar.2024

“Mamba promises similar performance (and crucially similar scaling laws) as the Transformer whilst being feasible at long sequence lengths (say 1 million tokens).”

Cat Links What I Learn Tag Links attention compression efficiency large language model linear algebra machine learning mamba Markov natural language processing neural network recurrent sequence state space transformer

Post navigation

What I Read: speed up code, NumbaPrev post

What I Read: Data Selection, LLMsNext post

Categories

Bayesian statistics Machine Learning Statistics What I Learn What I Make

Tags

artificial intelligence attention Bayesian chatbot cognition computer vision database data engineering data science deployment DevOps economics efficiency embedding generalization generative GPU graph healthcare image interpretability large language model latency linear algebra machine learning medicine MLOps monitoring natural language processing neural network neuroscience optimization pipeline probability Python recurrent regression reinforcement learning scalability software engineering SQL statistics training transformer unit test

Copyright © 2024 Andrew Fairless, Ph.D.. All Rights Reserved. | Simple Persona by Catch Themes

Scroll Up