This post is co-written with Jonathan Jung, Mike Band, Michael Chi, and Thompson Bliss at the National Football League. A coverage scheme refers to the rules and responsibilities of each football defender tasked with stopping an offensive pass. It is at the core of… Read More »Identifying defense coverage schemes in NFL’s Next Gen Stats Huan Song AWS Machine Learning Blog
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. AnalyzeDocument Signatures is a feature within Amazon Textract that offers the ability to automatically detect signatures on any document. This can reduce the need… Read More »Detect signatures on documents or images using the signatures feature in Amazon Textract Maran Chandrasekaran AWS Machine Learning Blog
Earth’s changing climate poses an increased risk of drought due to global warming. Since 1880, the global temperature has increased 1.01 °C. Since 1993, sea levels have risen 102.5 millimeters. Since 2002, the land ice sheets in Antarctica have been losing mass at a… Read More »Monitoring Lake Mead drought using the new Amazon SageMaker geospatial capabilities Xiong Zhou AWS Machine Learning Blog
Posted by Ted White and Ofer Naaman, Staff Research Scientists, Google Quantum AI The Google Quantum AI team is building quantum computers with superconducting microwave circuits, but much like a classical computer the superconducting processor at the heart of these computers is only part of… Read More »Amplification at the quantum limit Google AI Google AI Blog
The “weights” of a neural network is referred as “parameters” in PyTorch code and it is fine-tuned by optimizer during training. On the contrary, hyperparameters are the parameters of a neural network that is fixed by design and not tuned by training. Examples are… Read More »How to Grid Search Hyperparameters for PyTorch Models Adrian Tam MachineLearningMastery.com
Machine learning (ML) has become ubiquitous. Our customers are employing ML in every aspect of their business, including the products and services they build, and for drawing insights about their customers. To build an ML-based application, you have to first build the ML model… Read More »Optimize your machine learning deployments with auto scaling on Amazon SageMaker Mohan Gandhi AWS Machine Learning Blog
Posted by Jinsung Yoon and Sercan O. Arik, Research Scientists, Google Research, Cloud AI Team Anomaly detection (AD), the task of distinguishing anomalies from normal data, plays a vital role in many real-world applications, such as detecting faulty products from vision sensors in manufacturing, fraudulent… Read More »Unsupervised and semi-supervised anomaly detection with data-centric ML Google AI Google AI Blog
This post is co-written with Stephen Aylward, Matt McCormick, Brianna Major from Kitware and Justin Kirby from the Frederick National Laboratory for Cancer Research (FNLCR). Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email… Read More »Share medical image research on Amazon SageMaker Studio Lab for free Stephen Aylward AWS Machine Learning Blog
Posted by Sanjiv Kumar, VP and Google Fellow, Google Research (This is Part 4 in our series of posts covering different topical areas of research at Google. You can find other posts in the series here.) The explosion in deep learning a decade ago was… Read More »Google Research, 2022 & beyond: Algorithms for efficient deep learning Google AI Google AI Blog
Posted by Hannes Hapke and Robert Crowe To produce production-level machine learning models, TensorFlow provides a portfolio of libraries under the umbrella of TensorFlow Extended (TFX). With just a pip install, TFX already includes a number of versatile pipeline components – referred to as… Read More »Extend your TFX pipeline with TFX-Addons noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog