2016, Clustering and Deep Learning - Data Science Current

Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

ML @ CMU

NOVEMBER 7, 2024

In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. The major components of RELand are illustrated in Fig.

Clustering

Clustering Cross Validation Machine Learning Machine Learning

How to tackle lack of data: an overview on transfer learning

Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

Supervised Learning

Supervised Learning Machine Learning Machine Learning Deep Learning

The effectiveness of clustering in IIoT

Mlearning.ai

APRIL 10, 2023

How this machine learning model has become a sustainable and reliable solution for edge devices in an industrial network An Introduction Clustering (cluster analysis - CA) and classification are two important tasks that occur in our daily lives. Industrial Internet of Things (IIoT) The Constraints Within the area of Industry 4.0,

Clustering

Clustering Internet of Things Algorithm Machine Learning

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Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). Amazon SageMaker distributed training jobs enable you with one click (or one API call) to set up a distributed compute cluster, train a model, save the result to Amazon Simple Storage Service (Amazon S3), and shut down the cluster when complete.

Clustering

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

AWS

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Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

JANUARY 17, 2024

The underlying Deep Learning Container (DLC) of the deployment is the Large Model Inference (LMI) NeuronX DLC. He retired from EPFL in December 2016.nnIn He focuses on developing scalable machine learning algorithms. Qing has in-depth knowledge on the infrastructure optimization and Deep Learning acceleration.

AWS

AWS Python Machine Learning Machine Learning

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

Deep Learning Deep Learning Machine Learning Machine Learning

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. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

ML

ML ML Deep Learning Deep Learning

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., 2019) proposed a novel adversarial training framework for improving the robustness of deep learning-based segmentation models. 2018; Sitawarin et al.,

Deep Learning

Deep Learning Deep Learning Machine Learning Machine Learning

NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

Heartbeat

AUGUST 23, 2023

These algorithms help legal professionals swiftly discover essential information, speed up document review, and assure comprehensive case analysis through approaches such as document clustering and topic modeling. Natural language processing and machine learning for law and policy texts. Thomson Reuters Legal Executive Institute.

Natural Language Processing

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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. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

ML

ML ML Deep Learning Deep Learning

Best Practices for Managing Computer Vision Projects

DagsHub

MARCH 19, 2024

Tesla, for instance, relies on a cluster of NVIDIA A100 GPUs to train their vision-based autonomous driving algorithms. Selecting robust hardware and infrastructure, incorporating cloud services for scalable resources, and keeping algorithms and models updated with advancements in deep learning and AI to enhance accuracy is also essential.

Algorithm

Algorithm Deep Learning Deep Learning Data Engineering

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. 2016 ) yet About half of people who die by suicide visit their primary care provider (PCP) within 1 month of doing so, compared with fewer than 1 in 5 contacting specialty mental health.

Machine Learning

Machine Learning Machine Learning Data Science Natural Language Processing

Training the YOLOv8 Object Detector for OAK-D

PyImageSearch

MAY 1, 2023

And, of course, all of this wouldn’t have been possible without the power of Deep Neural Networks (DNNs) and the massive computation by NVIDIA GPUs. 2016) published the YOLO research community gem, “ You Only Look Once: Unified, Real-Time Object Detection, ” at the CVPR (Computer Vision and Pattern Recognition) Conference.

Deep Learning

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Data Science Current

Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action

How to tackle lack of data: an overview on transfer learning

Webinars

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The effectiveness of clustering in IIoT

Webinars

Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning

A review of purpose-built accelerators for financial services

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

Comparative Analysis: PyTorch vs TensorFlow vs Keras

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

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

NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

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

Best Practices for Managing Computer Vision Projects

Meet the Winners of the Youth Mental Health Narratives Challenge

Training the YOLOv8 Object Detector for OAK-D

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