Introducing beginners to the mechanics of machine learning – Miriam Posner

The Little Book of Deep Learning

The Little Book of Deep Learning

huggingface/ml-intern

🤗 ml-intern: an open-source ML engineer that reads papers, trains models, and ships ML models

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.

Web Machine Learning

Making Machine Learning a first-class web citizen

The Applied Machine Learning Collective of the Rockies

We're building a hands-on, community-led space where AI/ML practitioners actually grow together. A modern guild where newcomers learn from journeymen, journeymen sharpen their skills alongside experts, and everyone works on real problems that matter.

The Roadmap of Mathematics for Machine Learning

A complete guide to linear algebra, calculus, and probability theory

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

Learnable Programming

Here's a trick question: How do we get people to understand programming?

NeuroAI

Neuroscience, cognitive science, and AI are all questing for principles that help generalization. Learn more through a live, synchronous program designed for focused, hands-on learning.

Introducing Nested Learning: A new ML paradigm for continual learning

Ali Behrouz, Student Researcher, and Vahab Mirrokni, VP and Google Fellow, Google Research

What Parents Need to Know About AI in the Classroom | Stanford HAI

From immersive learning and personalized tutors to lesson plans and grading, AI is everywhere in K-12 education.

On AI as model organism for human learning

I've a pretty taste for paradox

Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics

What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post-hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.

Letta

Making machines that learn. Create stateful agents that remember everything, learn continuously, and improve themselves over time.

Quantifying the Carbon Emissions of Machine Learning

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.