Awesome AI Guidelines
This repository aims to simplify this by mapping the ecosystem of guidelines, principles, codes of ethics, standards and regulation being put in place around artificial intelligence.
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articles
CS197 Harvard: AI Research Experiences
Once we go from training one model to training hundreds of different models with different hyperparameters, we need to start organizing. We’re going to break down our organization into three pieces: experiment tracking, hyperparameter search, and configuration setup.
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These are all the youtube videos from the Summer of Math Exposition 2
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labml.ai Annotated PyTorch Paper Implementations
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations, and the website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better.
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Reinforcement Learning Algorithms: An Overview and Classification
The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques.
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#some2
#some2 380 videos • 351 channels
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Introduction to Diffusion Models for Machine Learning
The meteoric rise of Diffusion Models is one of the biggest developments in Machine Learning in the past several years. Learn everything you need to know about Diffusion Models in this easy-to-follow guide.
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Notebook
Awesome AI Guidelines
This repository aims to simplify this by mapping the ecosystem of guidelines, principles, codes of ethics, standards and regulation being put in place around artificial intelligence.
Link
Build a GNN-based real-time fraud detection solution using Amazon SageMaker, Amazon Neptune, and the Deep Graph Library
We focus on four tasks:
Processing a tabular transaction dataset into a heterogeneous graph dataset Training a GNN model using SageMaker Deploying the trained GNN models as a SageMaker endpoint Demonstrating real-time inference for incoming transactions Link
Contents Games and Puzzles as Multicomputational Systems
Multicomputation is one of the core ideas of the Wolfram Physics Project—and in particular is at the heart of our emerging understanding of quantum mechanics. But how can one get an intuition for what is initially the rather abstract idea of multicomputation? A good approach, I believe, is to see it in action in familiar systems and situations. And I explore here what seems like a particularly good example: games and puzzles.
Design Patterns in Machine Learning Code and Systems
Design patterns are not just a way to structure code. They also communicate the problem addressed and how the code or component is intended to be used.
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Multi node PyTorch Distributed Training Guide For People In A Hurry
PyTorch is designed to be the framework that’s both easy to use and delivers performance at scale. Indeed it has become the most popular deep learning framework, by a mile among the research community. However, despite some lengthy official tutorials and a few helpful community blogs, it is not always clear what exactly has to be done to make your PyTorch training to work across multiple nodes.
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The Physicalization of Metamathematics and Its Implications for the Foundations of Mathematics
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What is an awesome list?
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MLOPs Primer
Machine learning operations (MLOps) is becoming an exciting space as we figure out the best practices and technologies to deploy machine learning models in the real world. MLOps enable ML teams to build responsible and scalable machine learning systems and infrastructure.
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