🧠 A thoughtfully organized list of articles, books, courses, infographics and many more covering Artificial Intelligence,Algorithm, Machine Learning and Deep Learning gathered By Reza Hashemi 👍.
Fork this repo, add more content and a PR to merge that others can learn and share with others. More education 🏫 is what makes people live longer, not more money..
❗
exclamation: Fork this repo, add more content and a PR to merge that others can learn and share with others. More education school is what makes people live longer, not more money.
- A Field Guide To Genetic Programming - Riccardo Poli et al.
- Algorithmic Graph Theory
- Algorithms - Wikibooks
- Algorithms, 4th Edition - Robert Sedgewick and Kevin Wayne
- Algorithms and Automatic Computing Machines (1963) - B. A. Trakhtenbrot
- Algorithms and Complexity - Herbert S. Wilf (PDF)
- Algorithms Course Materials - Jeff Erickson
- Analysis and Design of Algorithms - Sandeep Sen, IIT Delhi
- Animated Algorithm and Data Structure Visualization (Resource)
- Annotated Algorithms in Python: Applications in Physics, Biology, and Finance - Massimo di Pierro
- Binary Trees (PDF)
- Clever Algorithms - Jason Brownlee
- CS Unplugged: Computer Science without a computer
- Data Structures - Prof. Subhashis Banerjee, IIT Delhi
- Data Structures (Into Java) - Paul N. Hilfinger (PDF)
- Data Structures and Algorithms: Annotated Reference with Examples - G. Barnett and L. Del Tongo (PDF)
- Data Structures Succinctly Part 1, Syncfusion (PDF, Kindle) (email address requested, not required)
- Data Structures Succinctly Part 2, Syncfusion (PDF, Kindle) (email address requested, not required)
- Elementary Algorithms - Larry LIU Xinyu
- Foundations of Computer Science - Al Aho and Jeff Ullman
- Geometry Algorithms - Dan Sunday
- Lectures Notes on Algorithm Analysis and Computational Complexity (Fourth Edition) - Ian Parberry (use form at bottom of license)
- LEDA: A Platform for Combinatorial and Geometric Computing - K. Mehlhorn et al.
- Linked List Basics (PDF)
- Linked List Problems (PDF)
- Matters Computational: Ideas, Algorithms, Source Code (PDF)
- Open Data Structures: An Introduction - Pat Morin
- Planning Algorithms
- Problems on Algorithms (Second Edition) - Ian Parberry (use form at bottom of license)
- Purely Functional Data Structures (1996) - Chris Okasaki (PDF)
- Sequential and parallel sorting algorithms
- Text Algorithms (PDF)
- The Algorithm Design Manual
- The Art of Computer Programming - Donald Knuth (fascicles, mostly volume 4)
- The Design of Approximation Algorithms (PDF)
- The Great Tree List Recursion Problem (PDF)
- Think Complexity (PDF)
- Berkeley University CS 61B: Data Structures
- IIT Bombay Foundation of Data Structures (CS213.1x)
- MIT's Design and Analysis of Algorithms (Spring 2012) - Dana Moshkovitz, Bruce Tidor
- MIT's Design and Analysis of Algorithms (Spring 2015) - Erik Demaine, Srini Devadas, Nancy Lynch
- MIT's Introduction to Algorithms (SMA 5503) (Fall 2005) - Charles Leiserson, Erik Demaine
- Princeton University Algorithms, Part 1
- Princeton University Algorithms, Part 2
- Stanford University Algorithms: Design and Analysis, Part 1
- Stanford University Algorithms: Design and Analysis, Part 2
- A First Course in Linear Algebra - Robert A. Beezer
- Advanced Algebra - Anthony W. Knapp (PDF)
- An Introduction to the Theory of Numbers - Leo Moser (PDF)
- Basic Algebra - Anthony W. Knapp (PDF)
- Basics of Algebra, Topology, and Differential Calculus (PDF)
- Bayesian Methods for Hackers - Cameron Davidson-Pilon
- Book of Proof - Richard Hammack (PDF)
- Calculus - Gilbert Strang (PDF)
- Calculus Made Easy - Silvanus P. Thompson (PDF)
- Category Theory for the Sciences
- CK-12 Probability and Statistics - Advanced
- Collaborative Statistics
- Computational and Inferential Thinking. The Foundations of Data Science
- Computational Geometry
- Concepts & Applications of Inferential Statistics
- Differential Equations - Paul Dawkins (PDF, use form to download)
- Elementary Differential Equations - William F. Trench (PDF)
- Essentials of Metaheuristics - Sean Luke
- Graph Theory
- Introduction to Probability - Charles M. Grinstead and J. Laurie Snell
- Introduction to Probability and Statistics Spring 2014
- Introduction to Proofs - Jim Hefferon
- Introduction to Statistical Thought - Michael Lavine
- Kalman and Bayesian Filters in Python
- Knapsack Problems - Algorithms and Computer Implementations - Silvano Martello and Paolo Toth
- Lecture Notes of Linear Algebra - Dr. P. Shunmugaraj, IIT Kanpur (PDF)
- Linear Algebra - Dr. Arbind K Lal, IIT Kanpur (PDF)
- Linear Algebra (PDF)
- Linear Algebra by Jim Hefferon - Jim Hefferon
- Mathematical Logic - an Introduction (PDF)
- Mathematics, MTH101A - P. Shunmugaraj, IIT Kanpur
- Non-Uniform Random Variate Generation - Luc Devroye (PDF)
- Number Theory - Holden Lee MIT
- OpenIntro Statistics
- Ordinary Differential Equations - Wikibooks
- Power Programming with Mathematica - David B. Wagner
- Probability and Statistics Cookbook
- Probability and Statistics EBook
- Statistics Done Wrong - Alex Reinhart
- Think Bayes: Bayesian Statistics Made Simple - Allen B. Downey
- Think Stats: Probability and Statistics for Programmers - Allen B. Downey (using Python)
- AI Sciences
- 100+ Free Data Science Book
- E-Books
- R for Data Science
- Building Machine Learning Systems with Python
- A Brief Introduction to Machine Learning for Engineers - Osvaldo Simeone (PDF)
- A Brief Introduction to Neural Networks
- A Course in Machine Learning (PDF)
- A First Encounter with Machine Learning (PDF)
- An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
- Bayesian Reasoning and Machine Learning
- Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Gaussian Processes for Machine Learning
- Information Theory, Inference, and Learning Algorithms
- Introduction to Machine Learning - Amnon Shashua
- Learn Tensorflow - Jupyter Notebooks
- Learning Deep Architectures for AI (PDF)
- Machine Learning
- Machine Learning, Neural and Statistical Classification
- Neural Networks and Deep Learning
- Probabilistic Models in the Study of Language (Draft, with R code)
- Reinforcement Learning: An Introduction (Draft) - Richard S. Sutton, Andrew G. Barto (PDF)
- Speech and Language Processing (3rd Edition Draft) - Daniel Jurafsky, James H. Martin (PDF)
- The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- The LION Way: Machine Learning plus Intelligent Optimization - Roberto Battiti, Mauro Brunato (PDF)
- The Python Game Book
- Introduction To Reverse Engineering Software
- Introductory Intel x86: Architecture, Assembly, Applications, & Alliteration
- Android Developer Fundamentals (Version 2) — Codelab
- Android Developer Fundamentals (Version 2) — Concepts
- Learn how to program: Android - Epicodus Inc.
- Material design
- Programming Cloud Services for Android Handheld Systems
- Programming Mobile Applications for Android Handheld Systems pt. 1
- Programming Mobile Applications for Android Handheld Systems pt. 2
- Programming Mobile Services for Android Handheld Systems: Communication
- Programming Mobile Services for Android Handheld Systems: Concurrency
- Udacity Android Course Catalog
- Learn how to program: C# - Epicodus Inc.
- C++ Tutorial
- Google's C++ Course
- Introduction to C++ (MIT's opencourseware)
- LearnCpp.com C++ Tutorial
- Microsoft edX Courses:
- Advanced Data Mining with Weka MOOC
- Data Mining with Weka MOOC
- Introduction to Python for Data Science
- More Data Mining with Weka MOOC
- The Analytics Edge
- Database Systems (MIT's opencourseware)
- Introduction to Databases (Stanford University)
- Convolutional Neural Networks for Visual Recognition
- Deep Learning for Natural Language Processing
- MIT 6.S094: Deep Learning for Self-Driving Cars
- Practical Deep Learning For Coders taught - Jeremy Howard
- Self-Paced Courses for Deep Learning
- Unsupervised Feature Learning and Deep Learning
- What is Deep Learning
- Bento Git Learning Track (Bento)
- Bento GitHub Learning Track (Bento)
- Git and GitHub for Poets
- GitHowTo
- How to Use Git and GitHub (Udacity)
- C9 : Functional Programming Fundamentals - Erik Meijer
- CIS 194: Introduction to Haskell - Brent Yorgey
- CS240h: Functional Systems in Haskell - Bryan O'Sullivan
- edX: Introduction to Functional Programming - Erik Meijer
- RWTH Aachen University: Functional Programming - Jürgen Giesl
- Bento CSS Learning Track (Bento)
- Bento HTML Learning Track (Bento)
- Build a Personal Website with Dash
- Build a responsive website with Webflow
- Build a SaaS landing page using Skeleton
- Build Dynamic Websites
- CSS Tutorial
- HTML Tutorial
- Learn how to program: CSS - Epicodus Inc.
- Learn HTML5 Programming From Scratch
- Central Connecticut State University, Introduction to CS Using Java
- Introduction to Java
- Java for Complete Beginners
- Java for Mobile Devices - Introducing Codename One
- Learn how to program: Java - Epicodus Inc.
- Object-Oriented programming with Java, part I
- Object-Oriented programming with Java, part II
- Princeton Algorithms, Part 1
- Princeton Algorithms, Part 2
- Problem Solving With Java
- Bento JavaScript Learning Track (Bento)
- Egghead.io
- Intro to JavaScript ES6 programming
- Learn how to program: JavaScript - Epicodus Inc.
- learn:query
- Angular.js Youtube Channel
- CodeCademy Angular
- egghead.io youtube channel: Learn AngularJS with Tutorial Videos & Training
- Shaping up with Angular.js
- Bento jQuery Track (Bento)
- Google's Machine Learning Crash Course
- Machine Learning Mini Bootcamp
- Pattern Recognition and Machine Learning
- Principles of Machine Learning By Microsoft
- Stanford University Machine Learning
- Hg Init: a Mercurial Tutorial - Joel Spolsky
- Advanced Data Structures
- Algorithm Design and Implementation
- Aml-2018 Ambient Intelligence (F. Corno - L. De Russis - A. Monge Roffarello)
- Berkeley's CS 61B: Data Structures
- Berkeley's CS 162: Operating Systems and Systems Programming
- Berkeley's CS 169: Software Engineering
- Berkeley's CS 194: What is an Operating System?
- Bits: The Computer Science of Digital Information
- Caltech's Learning From data
- Computer Graphics
- Embedded Software Safety (P. Koopman)
- FindLectures.com - Index of conference talks by language / topic
- LouvainX Paradigms of Computer Programming – Abstraction and Concurrency
- LouvainX Paradigms of Computer Programming – Fundamentals
- MIT 6.S099: Artificial General Intelligence
- MIT Numerical Methods (2014)
- MIT's Artificial Intelligence
- MIT's Computer Language Engineering
- MIT's Introduction to Algorithms
- MIT's Mathematics for Computer Science
- Principles of Reactive Programming
- Robotics I - (A. De Luca)
- Stanford Cryptography I
- Stanford Cryptography II
- Stanford SEE 229 - Machine Learning
- Stanford Engineering Everywhere EE103/CME103, EE104,EE263, EE363, EE364a, EE364b, EE365._ - Convex Optimization Prof Stephen P. Boyd
- Learn how to program: .NET - Epicodus Inc.
- Cornell's Data Structures and Functional Programming
- Introduction to Functional Programming in OCaml
- Learn how to program: PHP - Epicodus Inc.
- PHP & MySQL Tutorial
- An Introduction to Interactive Programming in Python (Part 1) (Coursera)
- An Introduction to Interactive Programming in Python (Part 2) (Coursera)
- Bento Python Learning Track (Bento)
- Berkeley's Structure and Interpretation of Computer Programs
- Codesdope
- Google's Python Course
- Introduction to Computer Science and Programming (MIT's opencourseware)
- Introduction to Python Programming (Udacity)
- Learn Python
- Learn Python - Free Interactive Python Tutorial
- Learn to program in Python
- Learn to Program: The Fundamentals (Coursera)
- Learn to Program Using Python (edX)
- Microsoft Virtual Academy
- Programming for Everybody
- Programming Foundations with Python (Udacity)
- Python Course
- Python Programming Tutorial
- Game Programming with QB64 - Terry Ritchie
- Learn how to program: Ruby - Epicodus Inc.
- RubyMonk - Interactive Ruby tutorials
- Advanced Software Construction in Java
- Agile Development Using Ruby on Rails - Advanced
- Agile Development Using Ruby on Rails - Basics
- Software Construction in Java
- SOC Verification Using SystemVerilog
- SystemVerilog - Learn basics of SystemVerilog for Hardware Verification
- SystemVerilog based UVM Methodology - Learn to build UVM based Testbenches in SystemVerilog
- Verilog Hardware Description Language - An Introductory Course
- Discover Flask - Full Stack Web Development with Flask
- Flask(A Python Microframework) Tutorial
- Free Code Camp
- The Odin Project - Learn Web Development for Free
- Introduction to Algorithms for Beginners and Aspiring Programmers
- Algorithms
- An introduction to Artificial Intelligence
- A beginner's guide to artificial intelligence, machine learning, and cognitive computing
- A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
- Which machine learning algorithm should I use?
- The Journey of a Machine Learning model from Building to Retraining
- An executive’s guide to AI by McKinsey&Company
- Machine Learning for Dummies, IBM Limited edition - 75 Pages
- Machine Learning Yearning by Andrew Ng - Signup for a free draft copy - Approx. 100 pages
- The Hundred-Page Machine Learning Book
- Introductory Guide to Artificial Intelligence
- DZone's Guide to Artificial Intelligence: Machine Learning and Predictive Analytics
- What is Machine Learning?
- Introducing Deep Learning with MATLAB
- Introduction to Tensorflow
- Python Experimental
- Cool Math For Parents
- Simple to advanced calculators for math and statistics, units conversion and more...
- Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data
- Algorithms and Data Structures Cheatsheet
- The machine learning algorithm cheat sheet - sas.com
- Machine Learning Cheatsheet
- Stanford Cs 229:
- Deep Learning: http://stanford.io/2BsQ91Q
- Supervised Learning: http://stanford.io/2nRlxxp
- Unsupervised Learning: http://stanford.io/2MmP6FN
- Machine Learning for dummies cheat sheet
- Deeplearning.ai | Coursera by Andrew Ng - 5 courses
- Machine Learning | Coursera by Andrew Ng
- fast.ai courses
- Deep Learning Part 1: Practical Deep Learning for Coders
- Deep Learning Part 2: Cutting Edge Deep Learning for Coders
- Should We Worry About Artificial Intelligence (AI)?
- The future of AI: IS YOUR JOB UNDER THREAT? - Businessleader
- AI "Technology Readiness" Infographic Source: CallaghanInnovation
- AI Timeline Source: Apttus
- AI detailed Timeline Source: Digital Intelligence Today
- Gym - Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.
- TensorFlow - An open source machine learning framework for everyone
- Seedbank - Collection of Interactive Machine Learning Examples
- R2D3 - A visual introduction to machine learning
