Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science

Introduction to Statistics and Basics of Mathematics for Data Science - The Hacker's Way

Math Parser: Java, C#, C++, Kotlin, Android, and all .NET platforms (Nuget, Maven, CMake). Supports .NET Framework, .NET Core, .NET Standard, Xamarin, and more. Features: rich built-in library of math functions, operators, constants. Flexible in user-defined arguments, functions. Expressions provided as plain text. Easy to use. Well documented.

Forward Mode Automatic Differentiation for Julia

New open-source cross-platform symbolic algebra library for C# and F#. Can be used for both production and research purposes.

Quantitative Interview Preparation Guide, updated version here ==>

Power calculator for Android. Solve some problem algebra and calculus.

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

Do maths like a normal person

a symbolic math expression evaluator for javascript

➗ A really micro micro-service for advanced math.

Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes

List of resources & possible pathway for the Math of Machine Learning and AI.

The Emmy Computer Algebra System.

An experimental computer algebra system written in Go

☕ Symja - computer algebra language & symbolic math library. A collection of popular algorithms implemented in pure Java.

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.

⌨ Importable dictionary for typing math symbols more easily on your Android phone by using keyboard shortcuts inspired by LaTeX

Reverse Mode Automatic Differentiation for Julia