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Predicting Die Hard fans with ML.NET and C#
This post will teach you how to predict the value of a boolean using ML.NET and a binary classification trainer. The code uses movie scores as an example.
ML.NET Model Builder November Updates | .NET Blog
ML.NET Model Builder brings numerous bug fixes and enhancements, including advanced data loading options and streaming training data from SQL
ML.NET September Updates | .NET Blog
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 automatically trains custom machine learning models for you based on your scenario and data.
Introduction to Supervised Machine Learning | Premier Developer
Sr. Application Development Manager / Customer Engineer and PhD, David Da-Teh Huang, provides an introduction to supervised machine learning Author Introduction: Dr. Huang obtained his PhD from Caltech in 1990 and has been with Microsoft for over 20 years. He is a Certified Azure DevOps Expert,
Jij and Toyota Tsusho: reducing carbon emissions with Azure Quantum - Microsoft Quantum
Jij Inc. and Toyota Tsusho are working together to begin tackling mobility and traffic challenges using quantum-inspired optimization in Azure Quantum.
Joel Grus – Fizz Buzz in Tensorflow
Posts and writings by Joel Grus
Bell's Theorem: The Quantum Venn Diagram Paradox
Featuring 3Blue1Brown Watch the 2nd video on 3Blue1Brown here: https://www.youtube.com/watch?v=MzRCDLre1b4 Support MinutePhysics on Patreon! http://www.patre...
PyTorch LSTM: Text Generation Tutorial
Key element of LSTM is the ability to work with sequences and its gating mechanism.
Chat Bot With PyTorch - NLP And Deep Learning - Python Tutorial (Part 1)
In this Python Tutorial we build a simple chatbot using PyTorch and Deep Learning. I will also provide an introduction to some basic Natural Language Process...
Jeff Prosise — Machine learning for C# developers: Introducing ML
Ближайшая конференция — DotNext 2020 Piter 15-18 июня, Online Подробности и билеты: https://bit.ly/dotnext2020piter .Need to incorporate ML and AI into your ...
ML.NET Model Builder is now a part of Visual Studio | .NET Blog
ML.NET is a cross-platform, machine learning framework for .NET developers. Model Builder is the UI tooling in Visual Studio that uses Automated Machine Learning (AutoML) to train and consume custom ML.NET models in your .NET apps. You can use ML.NET and Model Builder to create custom machine learning models without having prior machine learning experience and without leaving the .NET ecosystem.
TechCrunch is now a part of Verizon Media
TechCrunch is now a part of Verizon Media techcrunch Your data, your experience
Introduction to Geometric Deep Learning | Paperspace Blog
This article covers a thorough introduction to geometric deep learning, including interesting use-cases like graph segmentation, classification, and KGCNs.
Using ML.NET for deep learning on images in Azure | .NET Blog
Introduction In March 2020, ML.NET added support for training Image Classification models in Azure. Although the image classification scenario was released in late 2019, users were limited by the resources on their local compute environments. Training in Azure enables users to scale image classification scenarios by using GPU optimized Linux virtual machines.
Help us shape the future of deep learning in .NET | .NET Blog
Whether you're actively implementing deep learning now or just starting to learn about it, we would love to hear your feedback to shape upcoming support.
Panel discussion "NLP"
Data Science fwdays'19 conference: https://fwdays.com/en/event/data-science-fwdays-2019/review/natural-language-processing Moderator: Vitaliy Radchenko (Data...
How Coca-Cola Uses AI For Its Loyalty Campaigns - The Click Reader
Coca-Cola is a well-known soft drink manufacturing company which has been rapidly adopting artificial intelligence. This article explains how they do it.
Google Cloud releases COVID-19 data sets to foster coronavirus-fighting AI models
Google launched the COVID-19 Public Datasets program, which will provide coronavirus data sets to researchers at no charge.
AlphaGo - The Movie | Full Documentary
With more board configurations than there are atoms in the universe, the ancient Chinese game of Go has long been considered a grand challenge for artificial...
Open Source Data Science to Fight COVID-19 (Corona Virus) - ClosedLoop.ai
With the spread of COVID-19 becoming an evermore assertive force in our lives, the healthcare data science community has an opportunity to play an important role in the mitigation of this emerging pandemic. History has shown response to such diseases can drastically alter the worst effects of such diseases. Many cities have imposed social distancing …
Extending the Q# Compiler | Q# Blog
One of the most exciting things in our work is offering new ways for you to incorporate your own ideas and vision into our tools. In this blog post, I would like to highlight a feature that is especially interesting in that regard and came in with our end-of-January release: Custom compilation steps,
amzn/computer-vision-basics-in-microsoft-excel
Computer Vision Basics in Microsoft Excel (using just formulas) - amzn/computer-vision-basics-in-microsoft-excel
How to train a new language model from scratch using Transformers and Tokenizers
We’re on a journey to solve and democratize artificial intelligence through natural language.
Polynomial Regression from Scratch in Python
Machine learning is one of the hottest topics in computer science today. And not without a reason: it has helped us do things that couldn’t be done before like image classification, image generation and natural language processing. But all of it boils down to a really simple concept: you give the computer data and the computer then finds patterns in that data. This is called “learning” or “training”, depending on your point of view. These learnt patterns can be extrapolated to make predictions. How? That’s what we are looking at today.
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