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t-SNE (t-distributed stochastic neighbor embedding)

Dataconomy

t-SNE was developed by Laurens van der Maaten and Geoffrey Hinton in 2008 to visualize high-dimensional data. Machine learning and deep learning t-SNE is essential for visualizing outputs from neural networks, thus helping to understand model behavior and performance during development.

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Meet the Research Scientist: Shirley Ho

NYU Center for Data Science

What sets Dr. Ho apart is her pioneering work in applying deep learning techniques to astrophysics. She led the first effort to accelerate astrophysical simulations with deep learning. To view all our current Research Scientists, please visit the CDS Research Engineers & Scientists page on our website.

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[AI/ML] Keswani’s Algorithm for 2-player Non-Convex Min-Max Optimization

Towards AI

In particular, min-max optimisation is curcial for GANs [2], statistics, online learning [6], deep learning, and distributed computing [7]. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXivpreprint arXiv:1706.06083, 2017.[5] Lugosi, Prediction, Learning, and Games. Arjovsky, S.

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Getting Started with AI

Towards AI

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system.

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Through Knowledge Sharing to Singularity, Accelerated By LLMs

Towards AI

Fast forward to 2008, and we see the Github launch, providing developers with a platform to collaborate on their projects online. The whole machine learning industry since the early days was growing on open source solutions like scikit learn (2007) and then deep learning frameworks — TensorFlow (2015) and PyTorch (2016).

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. In 2017, the landmark paper “ Attention is all you need ” was published, which laid out a new deep learning architecture based on the transformer.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning Blog

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. On August 21, 2009, the Company filed a Form 10-Q for the quarter ended December 31, 2008.

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