Unlock the Potential of Object Detection with Red Hat OpenShift Data Science
Red Hat OpenShift Data Science gives data scientists and developers a powerful artificial intelligence/machine learning (AI/ML) platform for building intelligent applications. With OpenShift Data Science, data scientists and developers can rapidly develop, train, test, and iterate ML and deep learning (DL) models using their choice of certified tools in a fully supported environment—without waiting for […]
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Maryland School District to Use AI to Detect Guns
In a novel use of computer vision, Charles County Public Schools in Maryland is adding a computer vision system to their existing security camera system. Strangely absent from this news article is a discussion on ethics, civil liberties, or what happens when the AI makes a false positive.
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How to Train U-NET in Google Colab: A Step-by-Step Guide
Are you looking to train U-NET for semantic segmentation, background removal, or salient feature highlighting in Google Colab? This step-by-step guide from Augmented Startups will walk you through the entire process, from setting up the environment to loading and training your own dataset
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Doctor Strange Multiverse Using Computer Vision.
How to add Doctor Strange VFX in real time using Computer Vision.
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OpenCV & the Future of Computer Vision
This presentation provides an overview of recent developments and capabilities around OpenCV, The Open Source Computer Vision Library
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4 Computer Vision ideas to make a SMART FARM
Here’s an interesting take on what AI can do for agriculture. In the last 2 years, since I started showing on YouTube project prototypes built with computer vision I have been contacted by workers of different industries, and Farm is one of them. First they wanted to validate their idea, whether improving some processes inside […]
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How Deep Neural Networks Work
This is part of the End-to-End Machine Learning School Course 193, How Neural Networks Work at https://e2eml.school/193
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Ray A Framework for Scaling and Distributing Python & ML Applications | Anyscale
Modern machine learning (ML) workloads, such as deep learning and large-scale model training, are compute-intensive and require distributed execution. Ray is an open-source, distributed framework from U.C. Berkeley’s RISELab that easily scales Python applications and ML workloads from a laptop to a cluster, with an emphasis on the unique performance challenges of ML/AI systems. It […]
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How to Scale up your Pandas workflows with Modin
pandas is one of the most commonly used data science libraries in Python, with a convenient set of APIs to help data scientists prepare, analyze, and explore their data. However, despite its widespread adoption, pandas suffers from severe memory and performance issues on moderately large datasets. This presentation focuses on Modin, a fast, scalable drop-in […]
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The Modern Stack for ML Infrastructure
Metaflow was originally developed at Netflix to provide a user-friendly platform for a wide range of ML use cases from computer vision and NLP to classical statistics. Today, Metaflow is used by hundreds of companies from real estate and finance to biotech and drones. In this talk, we give a technical overview of Metaflow, showing […]
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