2015, Clustering and Deep Learning - Data Science Current

Announcing the ICDAR 2023 Competition on Hierarchical Text Detection and Recognition

Google Research AI blog

MARCH 7, 2023

In this competition, we invite researchers from around the world to build systems that can produce hierarchical annotations of text in images using words clustered into lines and paragraphs. Middle: Illustration of line clustering. Right: Illustration paragraph clustering. Samples from the HierText dataset.

Clustering

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Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

OCTOBER 5, 2023

Our high-level training procedure is as follows: for our training environment, we use a multi-instance cluster managed by the SLURM system for distributed training and scheduling under the NeMo framework. He focuses on developing scalable machine learning algorithms. Youngsuk Park is a Sr. He founded StylingAI Inc.,

AWS

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Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2015; Huang et al., an image) with the intention of causing a machine learning model to misclassify it (Goodfellow et al., 2012; Otsu, 1979; Long et al.,

Deep Learning

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Automation, Evolved: Your New Playbook For Smarter Knowledge Work

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Best Machine Learning Frameworks for ML Experts in 2023

Pickl AI

JANUARY 23, 2023

It is mainly used for deep learning applications. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. It also allows distributed training.

Machine Learning

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

OCTOBER 23, 2024

He focuses on Deep learning including NLP and Computer Vision domains. Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning.

AI

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Comparative Analysis: PyTorch vs TensorFlow vs Keras

Pickl AI

AUGUST 22, 2024

Introduction Deep Learning frameworks are crucial in developing sophisticated AI models, and driving industry innovations. By understanding their unique features and capabilities, you’ll make informed decisions for your Deep Learning applications.

Deep Learning

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

AWS Machine Learning Blog

NOVEMBER 19, 2024

In this builders’ session, learn how to pre-train an LLM using Slurm on SageMaker HyperPod. Explore the model pre-training workflow from start to finish, including setting up clusters, troubleshooting convergence issues, and running distributed training to improve model performance. You must bring your laptop to participate.

AWS

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Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. So for example, in 2015, fidget spinners were all the rage.

K-nearest Neighbors

K-nearest Neighbors Clustering Deep Learning Deep Learning

Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. So for example, in 2015, fidget spinners were all the rage.

K-nearest Neighbors

K-nearest Neighbors Clustering Deep Learning Deep Learning

Coactive AI’s CEO: quality beats quantity for data selection

Snorkel AI

APRIL 11, 2023

The unprecedented amount of available data has been critical to many of deep learning’s recent successes, but this big data brings its own problems. Active learning is a really powerful data selection technique for reducing labeling costs. So for example, in 2015, fidget spinners were all the rage.

K-nearest Neighbors

K-nearest Neighbors Clustering Deep Learning Deep Learning

Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

AWS Machine Learning Blog

FEBRUARY 24, 2023

This dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2019. Amazon Rekognition makes it easy to add image and video analysis into our applications, using proven, highly scalable, deep learning technology.

ML

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Netflix Movies and Series Recommendation Systems

PyImageSearch

JULY 3, 2023

Figure 4: The Netflix personalized home page generation problem (source: Alvino and Basilico, “Learning a Personalized Homepage,” Netflix Technology Blog , 2015 ). These features can be simple metadata or model-based features (extracted from a deep learning model), representing how good that video is for a member.

Deep Learning

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

AWS Machine Learning Blog

APRIL 18, 2023

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. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015.

ML

ML ML Deep Learning Deep Learning

Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

AWS Machine Learning Blog

JANUARY 13, 2023

They were admitted to one of 335 units at 208 hospitals located throughout the US between 2014–2015. FedML supports several out-of-the-box deep learning algorithms for various data types, such as tabular, text, image, graphs, and Internet of Things (IoT) data. Define the model.

AWS

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning Blog

APRIL 18, 2023

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. per diluted share, for the year ended December 31, 2015. per diluted share, for the year ended December 31, 2015.

ML

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Meet the Winners of the Youth Mental Health Narratives Challenge

DrivenData Labs

FEBRUARY 3, 2025

I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs. His journey in AI began in 2015 with a master's in computer vision for biomedical image analysis. Summary of approach: At first, I saw that there were only 4000 samples. Alejandro A.

Machine Learning

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Data Science Current

Announcing the ICDAR 2023 Competition on Hierarchical Text Detection and Recognition

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

Webinars

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Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Webinars

Best Machine Learning Frameworks for ML Experts in 2023

Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

Comparative Analysis: PyTorch vs TensorFlow vs Keras

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

Coactive AI’s CEO: quality beats quantity for data selection

Coactive AI’s CEO: quality beats quantity for data selection

Coactive AI’s CEO: quality beats quantity for data selection

Accelerate disaster response with computer vision for satellite imagery using Amazon SageMaker and Amazon Augmented AI

Netflix Movies and Series Recommendation Systems

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

Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2

Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

Meet the Winners of the Youth Mental Health Narratives Challenge

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