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Is FashionMNIST, a dataset of images of clothing items labeled by category, more similar to MNIST or to USPS, both of which are classification datasets of handwritten digits? This is a pretty hard question to answer, but the solution could have an impact on various aspects of machine learning. For example, it could change how […] The post Measuring
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Photo by Stephen Dawson on Unsplash The Definitive Guide to Designing Product Metrics Discussing Precision and Recall metrics, metric design interviews, and the metric lifecycle By Zachary Thomas in Editors’ Picks  — 11 min read Food photo created by valeria_aksakova — www.freepik.com A Must-Know New Clustering Algorithm for Disease Modelling This ...
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For over a decade, the Project InnerEye team at Microsoft Research Cambridge has been developing state-of-the-art machine learning methods for the automatic, quantitative analysis of three-dimensional medical images. An important application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. This tas
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Photo by Will Myers on Unsplash Anomaly detection involves identifying the differences, deviations, and exceptions from the norm in a dataset. It’s sometimes referred to as outlier detection. Anomaly detection is not a new concept or technique, it has been around for a number of years and is a common application of Machine Learning. The real world ...
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An easy and efficient way to explore a large quantity of images Photo by Jon Tyson on Unsplash As a data scientist, I often work on anti-fraud investigation missions. Exploration is therefore an essential part of the investigation. It allows one to become familiar with the subject of the analysis. I will detail here a simple, fast, efficient and re ...
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Since many students in my Stat 451: Introduction to Machine Learning and Statistical Pattern Classification class are relatively new to Python and NumPy, I was recently devoting a lecture to the latter. Since the course notes are based on an interactive Jupyter notebook file, which I used as a basis for the lecture videos, I thought it would be wor ...
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Learning about Data Science using the Stackoverflow survey Ada Lovelace following CRISP-DM [Public Domain] Introduction As someone looking to bring more data science to my work, I felt it would be interesting to look at the characteristics of those already in the field — and see if these differed from other developers. The Stackoverflow user survey ...
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Costs prediction of a Marketing Campaign (Data Cleaning & Feature Selection — Part II) A Data Science approach to predict the best candidates to be targeted for a marketing campaign Data Cleaning & Feature Selection — Gonçalo Guimarães Gomes About the project This article is the 2nd out of 3 of a Machine Learning — binary classification — project w ...
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I'm currently studying in Andrew Ng's famous course on ML and find it very well presented and informative. However, he understandably leaves out some of the more complicated mathematical stuff like showing the proof for partial derivatives or vectorization he gives. I'm a sort of calculus/math fan and would like to see those rigorous mathematical ...
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ML.NET is an open-source, cross-platform machine learning framework for .NET developers. It enables integrating machine learning into your .NET apps without requiring you to leave the .NET ecosystem or even have a background in ML or data science. ML.NET provides tooling (Model Builder UI in Visual Studio and the cross platform ML.NET CLI) that aut
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We're introducing to you the preview of Metrics Advisor, a new Cognitive Service, an AI analytics service that proactively monitors metrics and diagnoses issues. Learn More:  Metrics Advisor & Anomaly Detector Advisors Teams group Metrics Advisor Metrics Advisor Docs Create a Free account (Azure) Deep Learning vs. Machine Learning   Get Started wit
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In today's blog, I explore k-means clustering capabilities in R including algorithms, visualizations and methodologies to identify the optimal number of clusters ("k"). K-means clustering is an unsupervised machine learning tool to group s... The post Kmeans Clustering of Penguins first appeared on R-bloggers.
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Workshop announcement Because this year’s UseR 2020 in Munich couldn’t happen as an in-person event, I will be giving my workshop on Deep Learning with Keras and TensorFlow as an online event on Thursday, 8th of October (13:00 UTC / 15:00 CEST) You ... The post Free workshop on Deep Learning with Keras and TensorFlow first appeared on R-bloggers.
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Posted by Cam Askew and André Araujo, Software Engineers, Google Research Instance-level recognition (ILR) is the computer vision task of recognizing a specific instance of an object, rather than simply the category to which it belongs. For example, instead of labeling an image as “post-impressionist painting”, we’re interested in instance-level la ...
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Training to the test set is a type of overfitting where a model is prepared that intentionally achieves good performance on a given test set at the expense of increased generalization error. It is a type of overfitting that is common in machine learning competitions where a complete training dataset is provided and where only […] The post How to Tr
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Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or even developing a predictive model. As an approach to machine learning competitions, it is rightfully frowned upon, and most competition platforms impose limitations to prevent it, which is import
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Linear Discriminant Analysis is a linear classification machine learning algorithm. The algorithm involves developing a probabilistic model per class based on the specific distribution of observations for each input variable. A new example is then classified by calculating the conditional probability of it belonging to each class and selecting the
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Microsoft rolled out updates across its AI and machine learning cloud portfolio during its Ignite 2020 virtual conference.
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There are three different approaches to machine learning, depending on the data you have. You can go with supervised learning, semi-supervised learning, or unsupervised learning. In supervised learning you have labeled data, so you have outputs that you know for sure are the correct values for your inputs. That's like
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We are very excited to release the free tier of dunnhumby Model Lab this week as part of our partnership with Microsoft. dunnhumby Model Lab is an application that provides automated pipelines for deploying machine learning algorithms and has been used to build millions of models on behalf of our clients.Read the full story ...
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