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GitHub site for the book "Mathematical Methods in Data Science (with Python)" (soon to be published): mmids-textbook.github.io You can pre-order a print copy of the book here (with Amazon price guarantee): amzn.to/3KrbKvu
"Theory of Deep Learning" by Sanjeev Arora PDF: cs.princeton.edu/~arora/TheoryD…
Best Statistics Courses- mltut.com/best-course-on… @KirkDBorne @antgrasso @ronald_vanloon #MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras…
Fisher-Rao distance between multivariate normal distributions not known in closed-form: Approximate Rao's distance by discretizing *special curves c(t)* and approximate Rao's distance between nearby normals by the square root of Jeffreys' divergence. > arxiv.org/abs/2302.08175
🎯 Machine Learning Roadmap for 2025 Here’s a structured path to master Machine Learning — from the foundational math to building impactful projects. 📚 Learn the basics (Linear Algebra, Statistics, Programming) 🤖 Explore ML Algorithms (Supervised, Unsupervised,…
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RLHF is about human preference. My preference? Learn it by hand ✍️. I created a 15-step Excel walkthrough so you can trace RLHF by hand too. 🔽 Download: byhand.ai/rlhf
Level up your data science expertise! 📈 Mastering the core mathematics is the key to truly understanding machine learning algorithms and building effective models. This visual guide breaks down the 24 most important definitions, from Gradient Descent and Linear Regression to…
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10 Python Books for FREE — Master Python from Basics to Advanced clcoding.com/2025/10/10-pyt…
"Introduction to Machine Learning Systems" - FREE from MIT Press - Authored by Harvard Professor - 2048 Pages To Get It Simply: 1. Retweet & Reply "ML" 2. Follow so that I will DM you.
"Pen and Paper Exercises" in Machine Learning 211 Pages. 75+ Exercises w/ solutions. Comment to receive a FREE copy.
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Multivariate interpolation estimates unknown values based on multiple variables, making it useful in physics, finance, and machine learning. It allows for smoother approximations and more accurate predictions in multidimensional spaces. Common methods include bilinear, bicubic,…
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MIT's "Statistics for Applications" Lecture Videos: youtube.com/playlist?list=… Lecture Slides: ocw.mit.edu/courses/18-650…
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