Anemll/Anemll

Artificial Neural Engine Machine Learning Library

Anemll/examples/VARIABLE_CONTEXT.md at main · Anemll/Anemll

Artificial Neural Engine Machine Learning Library. Contribute to Anemll/Anemll development by creating an account on GitHub.

Personalized Machine Learning

This page contains collects information and supplementary material for my textbook Personalized Machine Learning:

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…

The Roadmap of Mathematics for Machine Learning

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

Machine Learning  |  Google for Developers

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.

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.

Machine Learning Glossary  |  Google for Developers

This glossary defines a wide range of machine learning terms, including those specific to TensorFlow and large language models.

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...

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.

AIR ‐ Uniform Resource Names for AI

A repository of models, textual inversions, and more - civitai/civitai

Scaling Laws for Neural Language Models

We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

The weather and climate science AI revolution isn’t revolutionary

Machine learning has its limits—how is it being used?

transformers/src/transformers/models/auto/tokenization_auto.py at 1d5296f2de31ecee4fdd789dc2b2fe068b320284 · huggingface/transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - huggingface/transformers