Awesome Artificial
Intelligence (AI) 
This is a curated list of Artificial Intelligence (AI) tools,
courses, books, lectures, and papers. AI, or Artificial Intelligence, is
a branch of computer science focused on creating machines that can
perform tasks requiring human-like intelligence. These tasks include
learning, reasoning, problem-solving, understanding natural language,
and recognizing patterns. AI aims to mimic human cognitive functions,
making machines capable of improving their performance based on
experience, adapting to new inputs, and performing human-like tasks.
Contributions are welcome. Connect on LinkedIn or X.
Contents
- Tools
- Courses
- Books
- Programming
- Philosophy
- Free Content
- Code
- Videos
- Learning
- Organizations
- Journals
- Competitions
- Newsletters
- Misc
Chat
- Chat GPT ChatGPT is a
free-to-use AI system. It allows users to engage in conversations, gain
insights, automate tasks, and witness the future of AI all in one
place.
- Gemini Gemini gives you
direct access to Google AI. Get help with writing, planning, learning,
and more.
- Claude Claude is a
family of foundational AI models that can be used in various
applications. You can talk directly with Claude at claude.ai to
brainstorm ideas, analyze images, and process long documents
Images
- Midjourney AI image
generation
- DALL·E 2 DALL·E 3 is an AI
system that can create realistic images and art from a natural-language
description.
Video
- Sora Sora is a text-to-video
AI model that can create realistic and imaginative scenes from text
instructions.
- Runway AI video generation
- Taskade Build, train, and
deploy AI agents to automate tasks, research, and collaborate in
real-time
Courses
- Introduction
to Artificial Intelligence (AI) - A high-level introduction to AI
from IBM on Coursera
- Introduction
to Generative AI - A beginner-level introduction to Generative AI
from Google on Coursera
- CS50’s Intro to
Artificial Intelligence - This course explores the concepts and
algorithms at the foundation of modern artificial intelligence
- MIT: Intro to Deep
Learning - A seven-day bootcamp designed in MIT to introduce deep
learning methods and applications
- Deep Blueberry:
Deep Learning book - A free five-weekend plan for self-learners to
learn the basics of deep-learning architectures like CNNs, LSTMs, RNNs,
VAEs, GANs, DQN, A3C and more
- Spinning Up in Deep
Reinforcement Learning - A free deep reinforcement learning course
by OpenAI
- MIT
Artificial Intelligence Videos - MIT AI Course
- Grokking
Deep Learning in Motion - Beginner’s course to learn deep learning
and neural networks without frameworks.
- Intro to Artificial
Intelligence - Learn the Fundamentals of AI. Course run by Peter
Norvig
- EdX
Artificial Intelligence - The course will introduce the basic ideas
and techniques underlying the design of intelligent computer
systems
- Artificial
Intelligence For Robotics - This class will teach you basic methods
in Artificial Intelligence, including probabilistic inference, planning
and search, localization, tracking and control, all with a focus on
robotics
- Machine Learning -
Basic machine learning algorithms for supervised and unsupervised
learning
- Deep
Learning - An Introductory course to Deep Learning using
TensorFlow.
- Stanford
Statistical Learning - Introductory course on machine learning
focusing on linear and polynomial regression, logistic regression and
linear discriminant analysis; cross-validation and the bootstrap, model
selection and regularization methods (ridge and lasso); nonlinear
models, splines and generalized additive models; tree-based methods,
random forests and boosting; support-vector machines.
- Knowledge
Based Artificial Intelligence - Georgia Tech’s course on Artificial
Intelligence focussing on Symbolic AI.
- Deep RL
Bootcamp Lectures - Deep Reinforcement Bootcamp Lectures - August
2017
- Machine
Learning Crash Course By Google Machine Learning Crash Course
features a series of lessons with video lectures, real-world case
studies, and hands-on practice exercises.
- Python Class By
Google This is a free class for people with a little bit of
programming experience who want to learn Python. The class includes
written materials, lecture videos, and lots of code exercises to
practice Python coding.
- Deep
Learning Crash Course In this liveVideo course, machine learning
expert Oliver Zeigermann teaches you the basics of deep learning.
- Artificial
Intelligence: A Modern Approach - Stuart Russell & Peter Norvig
- Paradigms Of
Artificial Intelligence Programming: Case Studies in Common Lisp -
Paradigms of AI Programming is the first text to teach advanced Common
Lisp techniques in the context of building major AI systems
- Reinforcement
Learning: An Introduction - This introductory textbook on
reinforcement learning is targeted toward engineers and scientists in
artificial intelligence, operations research, neural networks, and
control systems, and we hope it will also be of interest to
psychologists and neuroscientists.
- The
Cambridge Handbook Of Artificial Intelligence - Written for
non-specialists, it covers the discipline’s foundations, major theories,
and principal research areas, plus related topics such as artificial
life
- The Emotion
Machine: Commonsense Thinking, Artificial Intelligence, and the Future
of the Human Mind - In this mind-expanding book, scientific pioneer
Marvin Minsky continues his groundbreaking research, offering a
fascinating new model for how our minds work
- Artificial
Intelligence: A New Synthesis - Beginning with elementary reactive
agents, Nilsson gradually increases their cognitive horsepower to
illustrate the most important and lasting ideas in AI
- On
Intelligence - Hawkins develops a powerful theory of how the human
brain works, explaining why computers are not intelligent and how, based
on this new theory, we can finally build intelligent machines. Also
audio version available from audible.com
- How
To Create A Mind - Kurzweil discusses how the brain works, how the
mind emerges, brain-computer interfaces, and the implications of vastly
increasing the powers of our intelligence to address the world’s
problems
- Deep Learning -
Goodfellow, Bengio and Courville’s introduction to a broad range of
topics in deep learning, covering mathematical and conceptual
background, deep learning techniques used in industry, and research
perspectives.
- The
Elements of Statistical Learning: Data Mining, Inference, and
Prediction - Hastie and Tibshirani cover a broad range of topics,
from supervised learning (prediction) to unsupervised learning including
neural networks, support vector machines, classification trees and
boosting—the first comprehensive treatment of this topic in any
book.
- Deep
Learning and the Game of Go - Deep Learning and the Game of Go
teaches you how to apply the power of deep learning to complex
human-flavored reasoning tasks by building a Go-playing AI. After
exposing you to the foundations of machine and deep learning, you’ll use
Python to build a bot and then teach it the rules of the game.
- Deep
Learning for Search - Deep Learning for Search teaches you how to
leverage neural networks, NLP, and deep learning techniques to improve
search performance.
- Deep
Learning with PyTorch - PyTorch puts these superpowers in your
hands, providing a comfortable Python experience that gets you started
quickly and then grows with you as you—and your deep learning
skills—become more sophisticated. Deep Learning with PyTorch will make
that journey engaging and fun.
- Deep
Reinforcement Learning in Action - Deep Reinforcement Learning in
Action teaches you the fundamental concepts and terminology of deep
reinforcement learning, along with the practical skills and techniques
you’ll need to implement it into your own projects.
- Grokking
Deep Reinforcement Learning - Grokking Deep Reinforcement Learning
introduces this powerful machine learning approach, using examples,
illustrations, exercises, and crystal-clear teaching.
- Fusion in
Action - Fusion in Action teaches you to build a full-featured data
analytics pipeline, including document and data search and distributed
data clustering.
- Real-World
Natural Language Processing - Early access book on how to create
practical NLP applications using Python.
- Grokking
Machine Learning - Early access book that introduces the most
valuable machine learning techniques.
- Succeeding with
AI - An introduction to managing successful AI projects and applying
AI to real-life situations.
- Elements of AI (Part 1) -
Reaktor/University of Helsinki - An Introduction to AI is a free
online course for everyone interested in learning what AI is, what is
possible (and not possible) with AI, and how it affects our lives – with
no complicated math or programming required.
- Essential
Natural Language Processing - A hands-on guide to NLP with practical
techniques, numerous Python-based examples and real-world case
studies.
- Kaggle’s micro
courses - A series of micro courses by offering practical and
hands-on knowledge ranging from Python to Deep Learning.
- Transfer
Learning for Natural Language Processing - A book that gets you up
to speed with the relevant ML concepts and then dives into transfer
learning for NLP.
- (Stanford Deep Learning
Series][https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb]
- Amazon
Machine Learning Developer Guide - A book for ML developers which
introduces the ML concepts & strategies with lots of practical
usages.
- Machine Learning
Observability Course - Self-guided course covers the intuition,
math, and best practices for effective machine learning
observability.
- Machine
Learning for Humans - A series of simple, plain-English explanations
accompanied by math, code, and real-world examples.
Books
- Machine
Learning for Mortals (Mere and Otherwise) - Early access book that
provides basics of machine learning and using R programming
language.
- How
Machine Learning Works - Mostafa Samir. Early access book that
introduces machine learning from both practical and theoretical aspects
in a non-threatening way.
- MachineLearningWithTensorFlow2ed
is a book on general-purpose machine learning techniques, including
regression, classification, unsupervised clustering, reinforcement
learning, autoencoders, convolutional neural networks, RNNs, and LSTMs,
using TensorFlow 1.14.1.
- Serverless
Machine Learning - a book for machine learning engineers on how to
train and deploy machine learning systems on public clouds like AWS,
Azure, and GCP, using a code-oriented approach.
- The Hundred-Page Machine Learning
Book - all you need to know about Machine Learning in a hundred
pages, supervised and unsupervised learning, SVM, neural networks,
ensemble methods, gradient descent, cluster analysis and dimensionality
reduction, autoencoders and transfer learning, feature engineering and
hyperparameter tuning.
- Trust in
Machine Learning - a book for experienced data scientists and
machine learning engineers on how to make your AI a trustworthy partner.
Build machine learning systems that are explainable, robust,
transparent, and optimized for fairness.
- Generative
AI in Action - A book that shows exactly how to add generative AI
tools for text, images, and code, and more into your organization’s
strategies and projects..
Programming
Philosophy
- Super
Intelligence - Superintelligence asks the question: What happens
when machines surpass humans in general intelligence?
- Our
Final Invention: Artificial Intelligence And The End Of The Human
Era - Our Final Invention explores the perils of the heedless
pursuit of advanced AI. Until now, human intelligence has had no rival.
Can we coexist with beings whose intelligence dwarfs our own? And will
they allow us to?
- How
to Create a Mind: The Secret of Human Thought Revealed - Ray
Kurzweil, director of engineering at Google, explored the process of
reverse-engineering the brain to understand precisely how it works, then
applies that knowledge to create vastly intelligent machines.
- Minds,
Brains, And Programs - The 1980 paper by philosopher John Searle
that contains the famous ‘Chinese Room’ thought experiment. It is
probably the most famous attack on the notion of a Strong AI possessing
a ‘mind’ or a ‘consciousness’, and it is an interesting reading for
those interested in the intersection of AI and philosophy of mind.
- Gödel,
Escher, Bach: An Eternal Golden Braid - Written by Douglas
Hofstadter and taglined “a metaphorical fugue on minds and machines in
the spirit of Lewis Carroll”, this incredible journey into the
fundamental concepts of mathematics, symmetry and intelligence won a
Pulitzer Prize for Non-Fiction in 1979. A major theme throughout is the
emergence of meaning from seemingly ‘meaningless’ elements, like 1’s and
0’s, arranged in special patterns.
- Life
3.0: Being Human in the Age of Artificial Intelligence - Max
Tegmark, professor of Physics at MIT, discusses how Artificial
Intelligence may affect crime, war, justice, jobs, society and our very
sense of being human both in the near and far future.
Free Content
Code
- ExplainX-
ExplainX is a fast, lightweight, and scalable explainable AI framework
for data scientists to explain any black-box model to business
stakeholders.
- AIMACode - Source code for
“Artificial Intelligence: A Modern Approach” in Common Lisp, Java, and
Python. More to come.
- FANN - Fast Artificial
Neural Network Library, native for C
- FARGonautica -
Source code of Douglas Hosftadter’s Fluid Concepts and Creative
Analogies Ph.D. projects.
Videos
Learning
Organizations
Journals
Competitions
Newsletters
Misc
License
To the extent possible under law, Owain Lewis has waived all copyright
and related or neighbouring rights to this work.
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