ML-foundations
AcademicContent
ML-foundations | AcademicContent | |
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1 | 1 | |
2,984 | 2,647 | |
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5.4 | 0.0 | |
23 days ago | 5 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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ML-foundations
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Worried about Calculus
As others have said, you won't need calculus immediately, but it's important that you make a good attempt at learning up to Calc3. I also didn't have a math heavy undergrad so it took a lot of self-study for me, but it's possible. Simulation has a great math boot camp at the beginning to review everything but you'll want to be prepped with Calc before that because that class is all calculus based probability. Some other good resources are the 3Blue1Brown videos on YouTube. They have a great series for both calc & linear algebra to talk through all the intuition with visuals. I also really like John Krohns series because you code through the math which is very applicable for us in this program. I only did his linear Algebra, but he has a whole series with Calc and probability, too. https://github.com/jonkrohn/ML-foundations
AcademicContent
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Microsoft-Build-Student-Developer-Drop-in-day
Write code to play the Microsoft Match Game. Our version of the popular card game where you have to find two cards with the same image to make a match. But in our Game, no two tiles are the same. You have to match up the tiles that contain the same subject matter using the A.I. power of Azure Cognitive Services to analyse the images on each tile and to determine what subject of each image is. You start with a set of tiles face down and you get to turn over two tiles at a time in order to match tiles in pairs, but, unlike the usual version of the matching game, you are not looking for two identical images. You have to use Microsoft’s Cognitive Services to understand exactly what the content of each tile image is. Then you need to find another tile with the same content. Interested in taking part watch he following introduction Video read the game instructions and join the AI Gaming Challenge
What are some alternatives?
2D-Gaussian-Splatting - A 2D Gaussian Splatting paper for no obvious reasons. Enjoy!
FinancePy - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
ITC - Computer Science coursework and projects at Tec de Monterrey 👨🎓
wordlescraper - Combine wordle statistics metrics from various locations, data science to correlate scores with words, and a front end to display the results.
Computer-Science-Resources - This repository aims at providing the best resources for computer science students at one place. So they don't have to waste their precious time finding good resources.
the-elements-of-statistical-learning - My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
tutorials - Git Repo for Articles on Ergo Sum blog and the youtube channel https://www.youtube.com/channel/UCiie9CN--dazA7iT2sry5FA
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
algorithmica - A computer science textbook
rfsoc_studio - The Strathclyde RFSoC Studio Installer for PYNQ.