A complete guide to linear algebra, calculus, and probability theory
My Honest Review of Math Academy (Including Their Machine Learning Math Course)
Learning Math For Machine Learning on Math Academy
Introducing beginners to the mechanics of machine learning – Miriam Posner
Every year, I spend some time introducing students to the mechanics of machine learning with neural nets. I definitely don’t go into great depth; I usually only have one class for this. But I try to unpack at least some of the major concepts, so that ML isn’t quite such a black box.
Categories for Machine Learning
This seminar series seeks to promote the learning and use of Category Theory by Machine Learning Researchers
Personalized Machine Learning
This page contains collects information and supplementary material for my textbook Personalized Machine Learning:
ml5 - A friendly machine learning library for the web.
ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.
An Introduction to Statistical Learning
As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
Designing machine learning systems: an iterative process for production-ready applications
"Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references."--Amazon.com
Library: Faculty Guide to Generative AI: Welcome
Library: Faculty Guide to Generative AI: Welcome
Computational category theory
Computational category theory by D. E. Rydeheard, 1988, Prentice Hall edition, in English
Library: Faculty Guide to Generative AI: Detecting AI
Algorithmic Data Minimization for Machine Learning over...
Machine learning can analyze vast amounts of data generated by IoT devices to identify patterns, make predictions, and enable real-time decision-making. By processing sensor data, machine learning...
Datasheets for Datasets
The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose...
Lightweight Guide to understanding GRPO and RL principles
A beginner-friendly guide to Group Relative Policy Optimization (GRPO) training workflow without assuming prior RL knowledge.
