Machine-Learning-Based Prediction Models for AKI

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Acute Kidney Injury (AKI) is a common occurrence for critically ill patients in the ICU, and its early diagnosis has proven to be challenging. The accuracy of the online, machine-learning-based prediction model, AKIpredictor, was analysed for its use in a clinical setting. The study, which took place over five ICUs in Belgium, compared the predictions of AKIpredictor with physician predictions. The patient information for 250 individuals with no prior evidence of AKI or end-stage renal disease before ICU admission was used. Physicians then predicted AKI progression at three stages: at the initial admission, on the patient's first morning in the ICU and 24 hours later.



Microsoft, ISB partner to build AI solutions for better research and leadership

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Tech giant Microsoft and the Indian School of Business (ISB) on Friday announced that they have partnered to create an Artificial Intelligence (AI) Digital Lab, wherein the two organisations will collaborate in research. The research will use AI and Machine Learning (ML) to study relevant issues for business and public policy. In addition, they will introduce a new executive programme titled Leading Business Transformation in the Age of AI in October, which will equip business leaders with tools and strategies to transform their respective organisations into AI-driven organisations. The three-day non-technical programme is done under the purview of Microsoft's online AI Business School, which provides executive-level insights and practical and actionable guidance to build an end-to-end AI strategy. It will focus on managing the impact of AI on company strategy, culture and responsibility.


Visa has added new security capabilities for clients at no extra charge

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Visa rolls out a new suite of tools to fight fraud. The firm announced that the slate of offerings is meant to "help prevent and disrupt payment fraud" and is available to its clients without an additional fee or sign-up. Visa's solutions leverage AI to prevent and quickly put a stop to fraudulent transactions, which could help it keep pace with the AI-driven fraud tools introduced by firms like Mastercard, TSYS, and First Data. Offering fraud-focused tools is particularly important as fraud rises, especially as e-commerce grows more popular, so doing so could make Visa more attractive to firms. Global payment fraud losses are expected to rise 8.5% annually to reach $31 billion in 2020, costing merchants 7% of their annual revenue, according to data from First Data sent to Business Insider Intelligence.


Understanding a Dice Roll with Vision and Object Detection

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Pass the frames from the camera to the VNCoreMLRequest so it can make predictions using a VNImageRequestHandler object. The VNImageRequestHandler object handles image resizing and preprocessing as well as post-processing of your model's outputs for every prediction. To pass camera frames to your model, you first need to find the image orientation that corresponds to your device's physical orientation. If the device's orientation changes, the aspect ratio of the images can also change. Because you need to scale the bounding boxes for the detected objects back to your original image, you need to keep track of its size.


Creating Great Apps Using Core ML and ARKit - WWDC 2019 - Videos - Apple Developer

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Take a journey through the creation of an educational game that brings together Core ML, ARKit, and other app frameworks. See it all come to life in an interactive coding session.


Welcome! You are invited to join a webinar: Learning Lab 17: Anomaly Detection with H2O Machine Learning. After registering, you will receive a confirmation email about joining the webinar.

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Welcome to our BRAND NEW Learning Lab on Anomaly Detection for Fraud with H2O. We'll show you how we got a 0.944 AUC on Kaggle's Credit Fraud Challenge. Learning Lab 17 (Why Should I Sign Up?): - Learn about Anomaly Detection - What types exist and the problems it can be used to solve - Learn about Fraud Detection - Why Anomaly Detection helps - Apply an H2O IsolationForest model to financial data - We'll end up with a 0.944 AUC beating out most Supervised Learning Methods (e.g. XGBoost) - Get a 30-minute LIVE code-through - Have lots of FUN with Matt & David!


r/MachineLearning - [D] Interview with two senior data scientists at Microsoft about deep learning

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I was blown away by these two incredibly talented data scientists. Nothing inspires me more than having a conversation with people who are literally 10 times smarter than me. We discuss Mat's work on building out patterns for distributed deep learning on Azure. We talk about computer vision, interpretability, robustness, ML engineering and the democratisation of deep learning. Finishing off we discuss where the deep learning space is going in the next 5 years!


A Mathematical Model Unlocks the Secrets of Vision Quanta Magazine

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This is the great mystery of human vision: Vivid pictures of the world appear before our mind's eye, yet the brain's visual system receives very little information from the world itself. Much of what we "see" we conjure in our heads. "A lot of the things you think you see you're actually making up," said Lai-Sang Young, a mathematician at New York University. "You don't actually see them." Yet the brain must be doing a pretty good job of inventing the visual world, since we don't routinely bump into doors.


r/MachineLearning - [P] Time Series Analysis - Predicting Electricity Consumption using an LSTM network

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So, I compared the model with ARIMA and a few interesting findings. Firstly, there doesn't appear to be any seasonal component in the data - when decomposed with statsmodels, the series simply shows a straight line. Also, ARIMA showed a mean percentage error of 23%, whereas for LSTM it was just over 8%. The daily fluctuations in electricity consumption is quite volatile, so it looks like LSTM has an advantage over ARIMA here in that it is accounting for the inherent volatility in the series. While ARIMA would usually need to be combined with a model such as GARCH to estimate this volatility, the inherent nature of LSTM allows it to handle sequential data and in this case it looks like it's handling the volatility quite well.