GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Andreaz Kretz, the author of The Data Engineering Cookbook published the book on GitHub. His aim with this book was to provide a starting point for newbies in the data engineering world. He helps you to identify the important topics you need to learn about to become a successful Data Engine...
djangonosqldata-engineeringdata-scrapingdatabase-systemdata-science-projects UpdatedJan 2, 2025 Python Regular practice on Data Science, Machien Learning, Deep Learning, Solving ML Project problem, Analytical Issue. Regular boost up my knowledge. The goal is to help learner with learning resource on...
Prefect Cloud provides workflow orchestration for the modern data enterprise. By automating over 200 million data tasks monthly, Prefect empowers diverse organizations — from Fortune 50 leaders such as Progressive Insurance to innovative disruptors such as Cash App — to increase engineering productivity...
Realtime deployment Tutorial on Python time-series model deployment. Python for Data Science: A Beginner’s Guide Minimum Viable Study Plan for Machine Learning Interviews Understand and Know Machine Learning Engineering by Building Solid Projects 12 free Data Science projects to practice Python and Pan...
Computational Linear Algebra - fast.ai (Github) 10-600 Math Background for ML - CMU MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning 36-705 - Intermediate Statistics - Larry Wasserman, CMU (YouTube) Combinatorics - IISC Bangalore Advanced Engineering Mathema...
Scientific literature contains some of the most important information we have assembled as a species, such as how to treat diseases, solve difficult engineering problems, and answer many of the world’s challenges we are facing today. The entire body of scientific literature is growing at an enor...
The tile encoder individually projects all tiles into compact embeddings. The slide encoder then inputs the sequence of tile embeddings and generates contextualized embeddings taking into account the entire sequence using a transformer. The tile encoder is pretrained using DINOv2, the state-of-the-...
More information on these options is provided in the sections on Red Hat OpenShift and OpenShift data foundation Audience The intended audience for this document includes, but is not limited to, sales engineers, field consultants, professional services, IT managers, partner engineering, and customers...