stream:// data science

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,

Computer Vision Basics in Microsoft Excel (using just formulas) - amzn/computer-vision-basics-in-microsoft-excel

We’re on a journey to solve and democratize artificial intelligence through natural language.

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.

Here's a hands-on tutorial from bona-fide data scientist Dr. James McCaffrey of Microsoft Research to get you up to speed with machine learning development using C#, complete with code listings and graphics.

Amazon S3, Open Data Hub, Argo, Kubeflow, and Red Hat OpenShift are some of the popular tools that AI/ML Pipelines Using Open Data Hub and Kubeflow on Red Hat OpenShift uses. Learn more about the Language, Utilities, DevOps, and Business Tools in Red Hat, Inc.'s Tech Stack.

Here’s what you need to know about the largest data repository in the world

Work on these 23 amazing deep learning project ideas such as image caption generator, human pose estimation, etc to enhance your skills & become an expert.

Facebook AI has developed the first neural network that uses symbolic reasoning to solve advanced mathematics problems.

Overcome the complexity of building, training, and deploying machine learning models, from the cloud to the edge. Explore machine learning fundamentals, machine learning with Python, techniques for improving models, and developing viable frameworks for your machine learning solutions. In ...