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Using Amazon SageMaker with Point Clouds: Part 1- Ground Truth for 3D labeling Isaac Privitera AWS Machine Learning Blog

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​ In this two-part series, we demonstrate how to label and train models for 3D object detection tasks. In part 1, we discuss the dataset we’re using, as well as any preprocessing steps, to understand and label data. In part 2, we walk through how… Read More »Using Amazon SageMaker with Point Clouds: Part 1- Ground Truth for 3D labeling Isaac Privitera AWS Machine Learning Blog

Real-time fraud detection using AWS serverless and machine learning services Giedrius Praspaliauskas AWS Machine Learning Blog

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​ Online fraud has a widespread impact on businesses and requires an effective end-to-end strategy to detect and prevent new account fraud and account takeovers, and stop suspicious payment transactions. Detecting fraud closer to the time of fraud occurrence is key to the success of… Read More »Real-time fraud detection using AWS serverless and machine learning services Giedrius Praspaliauskas AWS Machine Learning Blog

How Vodafone Uses TensorFlow Data Validation in their Data Contracts to Elevate Data Governance at Scale noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

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​ Posted by Amandeep Singh (Vodafone), Max Vökler (Google Cloud) Vodafone leverages Google Cloud to deploy AI/ML use cases at scale As one of the largest telecommunications companies worldwide, Vodafone is working with Google Cloud to advance their entire data landscape, including their data lake,… Read More »How Vodafone Uses TensorFlow Data Validation in their Data Contracts to Elevate Data Governance at Scale noreply@blogger.com (TensorFlow Blog) The TensorFlow Blog

Self Supervision Does Not Help Natural Language Supervision at Scale Apple Machine Learning Research

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​Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks. Recent works such as M3AE [31] and SLIP [64] have suggested that these approaches can be effectively combined, but… Read More »Self Supervision Does Not Help Natural Language Supervision at Scale Apple Machine Learning Research

Artificial Intelligence vs Machine Learning vs Deep Learning Narender Kumar Spark By {Examples}

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​ We’ve all heard of the possibilities of artificial intelligence, machine learning, and deep learning. There have been many situations where artificial intelligence has made a measurable impact on an organization, and there have also been situations where organizations have wasted millions of dollars on… Read More »Artificial Intelligence vs Machine Learning vs Deep Learning Narender Kumar Spark By {Examples}

RGI: Robust GAN-inversion for Mask-free Image Inpainting and Unsupervised Pixel-wise Anomaly Detection Apple Machine Learning Research

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​Generative adversarial networks (GANs), trained on a large-scale image dataset, can be a good approximator of the natural image manifold. GAN-inversion, using a pre-trained generator as a deep generative prior, is a promising tool for image restoration under corruptions. However, the performance of GAN-inversion can… Read More »RGI: Robust GAN-inversion for Mask-free Image Inpainting and Unsupervised Pixel-wise Anomaly Detection Apple Machine Learning Research

PAEDID: Patch Autoencoder-based Deep Image Decomposition for Unsupervised Anomaly Detection Apple Machine Learning Research

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​Unsupervised pixel-level defective region segmentation is an important task in image-based anomaly detection for various industrial applications. The state-of-the-art methods have their own advantages and limitations: matrix-decomposition-based methods are robust to noise but lack complex background image modeling capability; representation-based methods are good at defective… Read More »PAEDID: Patch Autoencoder-based Deep Image Decomposition for Unsupervised Anomaly Detection Apple Machine Learning Research