Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
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Updated
Apr 17, 2024 - Jupyter Notebook
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way
The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
Math Parser Java Android C# .NET/MONO (.NET Framework, .NET Core, .NET Standard, .NET PCL, Xamarin.Android, Xamarin.iOS) CLS Library - a super easy, rich and flexible mathematical expression parser (expression evaluator, expression provided as plain text / strings) for JAVA and C#. Main features: rich built-in library of operators, constants, ma…
Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
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Complete path for a beginner to become a Machine Learning Scientist!
Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
☕ Symja - computer algebra language & symbolic math library. A collection of popular algorithms implemented in pure Java.
a symbolic math expression evaluator for javascript
VIP cheatsheets for Stanford's CME 102 Ordinary Differential Equations for Engineers
New open-source cross-platform symbolic algebra library for C# and F#. Can be used for both production and research purposes.
Open-source project hosted at https://makeuseofdata.com to crowdsource a robust collection of notes related to data science (math, visualization, modeling, etc)
Reverse Mode Automatic Differentiation for Julia
A collection of Mathematics + CS + what have you related books collected over the years for school 🎓 and personal reading 📚.
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