AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. Choice of compute AWS provides PyTorch 2.0 With the recent PyTorch 2.0 Refer to PyTorch 2.0: torch.compile + bf16 + fused AdamW.

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AWS Machine Learning Blog

OCTOBER 17, 2024

Amazon SageMaker Studio is the latest web-based experience for running end-to-end machine learning (ML) workflows. This can be useful for organizations that want to provide a centralized storage solution for their ML projects across multiple SageMaker Studio domains. In her free time, Irene enjoys traveling and hiking.

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Viso.ai

FEBRUARY 28, 2023

And why is OpenCV so popular in the Computer Vision Industry? Hence, the world’s leading companies across industries use OpenCV to develop their computer vision systems. What is Computer Vision? Leading organizations use it to build, deploy and scale real-world computer vision applications.

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AWS Machine Learning Blog

JUNE 15, 2023

With a background in AI/ML, data science, and analytics, Yunfei helps customers adopt AWS services to deliver business results. He designs AI/ML and data analytics solutions that overcome complex technical challenges and drive strategic objectives. About the authors Yunfei Bai is a Senior Solutions Architect at AWS.

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Viso.ai

APRIL 29, 2024

It was introduced by Geoffrey Hinton and his team in 2012, and marked a key event in the history of deep learning, showcasing the strengths of CNN architectures and its vast applications. Performance and Impact The AlexNet architecture dominated in 2012 by achieving a top-5 error rate of 15.3%, significantly lower than the runner-up’s 26.2%.

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AWS Machine Learning Blog

APRIL 5, 2023

Amazon Rekognition makes it easy to add this capability to your applications without any machine learning (ML) expertise and comes with various APIs to fulfil use cases such as object detection, content moderation, face detection and analysis, and text and celebrity recognition, which we use in this example.

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Mlearning.ai

MARCH 9, 2023

2012; Otsu, 1979; Long et al., Methodology In this study, we used the publicly available PASCAL VOC 2012 dataset (Everingham et al., The MBD model was trained on the training set of the PASCAL VOC 2012 dataset, and the resulting model was used to segment the selected images from the validation set. References: Arbeláez, P.,

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AWS Machine Learning Blog

MAY 10, 2023

Jupyter notebooks are highly favored by data scientists for their ability to interactively process data, build ML models, and test these models by making inferences on data. Durga Sury is an ML Solutions Architect on the Amazon SageMaker Service SA team. She is passionate about making machine learning accessible to everyone.

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AWS Machine Learning Blog

DECEMBER 14, 2023

Amazon SageMaker Studio offers a broad set of fully managed integrated development environments (IDEs) for machine learning (ML) development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code Open Source), and RStudio. It’s attached to a ML compute instance whenever a Space is run.

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Hal Daumé III

NOVEMBER 8, 2016

The last time I taught this class was in 2012. A silly example is the curation of older datasets for object detection in computer vision, which (I understand) decided on which objects to focus on by asking five year old relatives of the researchers constructing the datasets to name all the objects they could see.

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Heartbeat

AUGUST 21, 2023

AlexNet is a more profound and complex CNN architecture developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. This helps avoid disappearing gradients in very deep networks, allowing ResNet to attain cutting-edge performance on a wide range of computer vision applications.

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AWS Machine Learning Blog

MARCH 30, 2023

Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. You can get started without any prior ML experience, using APIs to easily build sophisticated personalization capabilities in a few clicks. mkdir $data_dir !cd

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AWS Machine Learning Blog

APRIL 22, 2024

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. He has worked on projects in different domains, including MLOps, computer vision, and NLP, involving a broad set of AWS services.

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AWS Machine Learning Blog

JULY 2, 2024

He is focused on computer vision use cases and helping customers across EMEA accelerate their ML journey. With a decade of experience at Amazon, having joined in 2012, Kshitiz has gained deep insights into the cloud computing landscape. Chris Pecora is a Generative AI Data Scientist at Amazon Web Services.

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Mlearning.ai

JUNE 18, 2023

In this article we show how to use Google Colab perform transfer learning on YOLO , a well known deep learning computer vision model written in C and CUDA. Summary In this demonstration we showed how to use Google Colab’s GPUs to train the deep learning model YOLO, a computer vision model written in C.

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Heartbeat

DECEMBER 8, 2023

Action: Wikipedia Action Input: "Yann LeCun" Observation: Page: Yann LeCun Summary: Yann André LeCun ( lə-KUN, French: [ləkœ̃]; originally spelled Le Cun; born 8 July 1960) is a Turing Award winning French computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience.

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AWS Machine Learning Blog

MARCH 12, 2024

Although this post only included natural language processing of sensitive data, you can modify this architecture to support alternate LLMs supporting audio, computer vision, or multi-modalities. Liv d’Aliberti is a researcher within the Leidos AI/ML Accelerator under the Office of Technology. Chris Renzo is a Sr.

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AWS Machine Learning Blog

APRIL 16, 2024

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. or later image versions.

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Applied Data Science

MARCH 11, 2022

Back in 2012 things were quite different. It is no wonder then that the field of computer vision became a main driver of progress in AI. Techniques developed in NLP, such as the Transformer architecture, are useful in very diverse fields such as computer vision and reinforcement learning. This cat does not exist.

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Lexalytics

APRIL 5, 2021

While working as an RA in the computer vision group, I had the opportunity to sit in a robotic Humvee as it used Pomerleau’s code to drive around the University of Massachusetts’ stadium.) For example, Dean Pomerleau used them to create a system that learned to drive a car [ 12 ]. (I So, whatever did happen to neural networks?

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Topbots

JANUARY 11, 2023

This potential is made clear by MacArthur Genius awardee Daphne Koller: We were able to identify, — using ML (machine learning) — patterns [within the liver tissue] that correspond with known genetic drivers of disease. Human pathologists couldn’t see those patterns because they don’t even know what to look for. Enjoy this article?

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AWS Machine Learning Blog

APRIL 23, 2024

We are excited to announce two new capabilities in Amazon SageMaker Studio that will accelerate iterative development for machine learning (ML) practitioners: Local Mode and Docker support. ML model development often involves slow iteration cycles as developers switch between coding, training, and deployment.

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Heartbeat

DECEMBER 14, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice-President, Chief AI Scientist at Meta.He

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Viso.ai

DECEMBER 21, 2023

Over the past decade, the field of computer vision has experienced monumental artificial intelligence (AI) breakthroughs. This blog will introduce you to the computer vision visionaries behind these achievements. Each of these individuals serves as an inspiration for aspiring AI and ML engineers breaking into the field.

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