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
Machine Learning | Google for Developers
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
Machine Learning Algorithms, Applications: Key Methods, Real-World Uses
Key Takeaways Traditional algorithms such as regression, decision trees, boosting, clustering, and PCA remain highly useful for practical ML problems.Neural net
Optimal and Diffusion Transports in Machine Learning
Slides for a general introduction to the use of Optimal Transport methods in learning, with an emphasis on diffusion models, flow matching, training 2 l…
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.
What Are Machine Learning Algorithms? | IBM
A machine learning algorithm is the procedure and mathematical logic through which an AI model learns patterns in training data and applies to them to new data.
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.
Machine Learning Tutorial - GeeksforGeeks
Your All-in-One Learning Portal: GeeksforGeeks is a comprehensive educational platform that empowers learners across domains-spanning computer science and programming, school education, upskilling, commerce, software tools, competitive exams, and more.
Crowdsourcing a list of general resources on AI and mathematics
Given current events, I think it is worthwhile to start collecting useful online resources with regards to AI and mathematics in general. I will start a list below, but ask contributors to contribu…
What is Machine Learning? | IBM
Machine learning is the subset of AI focused on algorithms that analyze and “learn” the patterns of training data in order to make accurate inferences about new data.
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
