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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning Blog

This year, generative AI and machine learning (ML) will again be in focus, with exciting keynote announcements and a variety of sessions showcasing insights from AWS experts, customer stories, and hands-on experiences with AWS services. Visit the session catalog to learn about all our generative AI and ML sessions.

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Getting Started with Docker for Machine Learning

Flipboard

Home Table of Contents Getting Started with Docker for Machine Learning Overview: Why the Need? How Do Containers Differ from Virtual Machines? Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps. How Do Containers Differ from Virtual Machines?

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Machine Learning and Language (ML²) at CDS: Moving NLP Forward

NYU Center for Data Science

Building on this momentum is a dynamic research group at the heart of CDS called the Machine Learning and Language (ML²) group. By 2020, ML² was a thriving community, primarily known for its recurring speaker series where researchers presented their work to peers. What does it mean to work in NLP in the age of LLMs?

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Build a crop segmentation machine learning model with Planet data and Amazon SageMaker geospatial capabilities

AWS Machine Learning Blog

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

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Inductive biases of neural network modularity in spatial navigation

ML @ CMU

We hypothesize that this architecture enables higher efficiency in learning the structure of natural tasks and better generalization in tasks with a similar structure than those with less specialized modules. What are the brain’s useful inductive biases?

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Tensor Processing Units (TPUs)

Dataconomy

Tensor Processing Units (TPUs) represent a significant leap in hardware specifically designed for machine learning tasks. They are essential for processing large amounts of data efficiently, particularly in deep learning applications. TPUs are specialized hardware designed to accelerate and optimize machine learning workloads.

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Achieving scalable and distributed technology through expertise: Harshit Sharan’s strategic impact

Dataconomy

In his thesis, A Context-Based Cross-Domain Collaborative Filtering Approach in Folksonomies , Harshit explored the intricacies of machine learning and recommendation systems, laying a solid foundation for his contributions to scalable systems and marketing technology.