This is a curated list of Artificial Intelligence (AI) courses, books, video lectures and papers.
- Courses 265
- Books 71
- Programming 27
- Philosophy 9
- Free Content 35
- Code 32
- Videos 59
- Learning 13
- Organizations 3
- Journals 5
- Competitions 12
- Newsletters 9
- Misc 7
- MIT: Intro to Deep Learning 90 – A seven day bootcamp designed in MIT to introduce deep learning methods and applications
- Deep Blueberry: Deep Learning book 37 – A free five-weekend plan to 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 10 – A free deep reinforcement learning course by OpenAI
- MIT Artifical Intelligence Videos 34 – MIT AI Course
- Grokking Deep Learning in Motion 7 – Beginner’s course to learn deep learning and neural networks without frameworks.
- Intro to Artificial Intelligence 12 – Learn the Fundamentals of AI. Course run by Peter Norvig
- EdX Artificial Intelligence 5 – The course will introduce the basic ideas and techniques underlying the design of intelligent computer systems
- Artificial Intelligence For Robotics 11 – 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 18 – Basic machine learning algorithms for supervised and unsupervised learning
- Neural Networks For Machine Learning 14 – Algorithmic and practical tricks for artifical neural networks.
- Deep Learning 11 – An Introductory course to the world of Deep Learning.
- Stanford Statistical Learning 6 – 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 3 – Georgia Tech’s course on Artificial Intelligence focussing on Symbolic AI.
- Deep RL Bootcamp Lectures 8 – Deep Reinforcement Bootcamp Lectures – August 2017
- Machine Learning Crash Course By Google 8 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 9 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 8 In this liveVideo course, machine learning expert Oliver Zeigermann teaches you the basics of deep learning.
- Artificial Intelligence: A Modern Approach 2 – Stuart Russell & Peter Norvig
- Also consider browsing the list of recommended reading 1, divided by each chapter in “Artificial Intelligence: A Modern Approach”.
- Paradigms Of Artificial Intelligence Programming: Case Studies in Common Lisp 2 – 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 4 – 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 3 – 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 2– 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 1 – Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI
- On Intelligence 4 – 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 1
- How To Create A Mind 3 – 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 3 – 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 3 – 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 7 – 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 2 – Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance.
- Deep Learning with PyTorch 13 – 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 3 – 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 6 – Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching.
- Fusion in Action 3 – 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 6 – Early access book on how to create practical NLP applications using Python.
- Grokking Machine Learning 6 – Early access book that introduces the most valuable machine learning techniques.
- Succeeding with AI 1 – An introduction to managing successful AI projects and applying AI to real-life situations.
- Elements of AI (Part 1) – Reaktor/University of Helsinki 2 – 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 4 – A hands-on guide to NLP with practical techniques, numerous Python-based examples and real-world case studies.
- Kaggle’s micro courses 3 – A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning.
- Machine Learning for Mortals (Mere and Otherwise) 4 – Early access book that provides basics of machine learning and using R programming language.
- How Machine Learning Works 2 – Mostafa Samir. Early access book that introduces machine learning from both practical and theoretical aspects in a non-threating way.
- MachineLearningWithTensorFlow2ed 8 – a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
- Prolog Programming For Artificial Intelligence 5 – This best-selling guide to Prolog and Artificial Intelligence concentrates on the art of using the basic mechanisms of Prolog to solve interesting AI problems.
- AI Algorithms, Data Structures and Idioms in Prolog, Lisp and Java 5 – PDF here 3
- Python Tools for Machine Learning 11
- Python for Artificial Intelligence 10
- Super Intelligence 2 – Superintelligence asks the questions: What happens when machines surpass humans in general intelligence. A really great book.
- 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 1 – 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 philospher John Searle that contains the famous ‘Chinese Room’ thought experiment. Probably the most famous attack on the notion of a Strong AI possessing a ‘mind’ or a ‘consciousness’, and 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 wonderful journey into the the fundamental concepts of mathematics,symmetry and intelligence won a Pulitzer Price 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 2 – 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.
- Foundations Of Computational Agents 1 – This book is published by Cambridge University Press, 2010
- The Quest For Artificial Intelligence – This book traces the history of the subject, from the early dreams of eighteenth-century (and earlier) pioneers to the more successful work of today’s AI engineers.
- Stanford CS229 – Machine Learning 7 – This course provides a broad introduction to machine learning and statistical pattern recognition.
- Computers and Thought: A practical Introduction to Artificial Intelligence 1 – The book covers computer simulation of human activities, such as problem solving and natural language understanding; computer vision; AI tools and techniques; an introduction to AI programming; symbolic and neural network models of cognition; the nature of mind and intelligence; and the social implications of AI and cognitive science.
- Society of Mind 1 – Marvin Minsky’s seminal work on how our mind works. Lot of Symbolic AI concepts have been derived from this basis.
- Artificial Intelligence and Molecular Biology 2 – The current volume is an effort to bridge that range of exploration, from nucleotide to abstract concept, in contemporary AI/MB research.
- Brief Introduction To Educational Implications Of Artificial Intelligence – This book is designed to help preservice and inservice teachers learn about some of the educational implications of current uses of Artificial Intelligence as an aid to solving problems and accomplishing tasks.
- Encyclopedia: Computational intelligence – Scholarpedia is a peer-reviewed open-access encyclopedia written and maintained by scholarly experts from around the world.
- Ethical Artificial Intelligence – a book by Bill Hibbard that combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence.
- Golden Artificial Intelligence – a cluster of pages on artificial intelligence and machine learning.
- R2D3 1 – A website with explanations on topics from Machine Learning to Statistics. All helped with beautiful animated infographics and real life examples. Available in various languages.
- AIMACode 5 – Source code for “Artificial Intelligence: A Modern Approach” in Common Lisp, Java, Python. More to come.
- FANN 3 – Fast Artificial Neural Network Library, native for C
- FARGonautica 1 – Source code of Douglas Hosftadter’s Fluid Concepts and Creative Analogies Ph.D. projects.
- A tutorial on Deep Learning 6
- Basics of Computational Reinforcement Learning
- Deep Reinforcement Learning 2
- Intelligent agents and paradigms for AI 1
- The Unreasonable Effectiveness Of Deep Learning – The Director of Facebook’s AI Research, Dr. Yann LeCun gives a talk on deep convolutional neural networks and their applications to machine learning and computer vision
- AWS Machine Learning in Motion 1– This interactive liveVideo course gives you a crash course in using AWS for machine learning, teaching you how to build a fully-working predictive algorithm.
- Deep Learning with R in Motion 3-Deep Learning with R in Motion teaches you to apply deep learning to text and images using the powerful Keras library and its R language interface.
- Grokking Deep Learning in Motion-Grokking Deep Learning in Motion will not just teach you how to use a single library or framework, you’ll actually discover how to build these algorithms completely from scratch!
- Reinforcement Learning in Motion – This liveVideo breaks down key concepts like how RL systems learn, how to sense and process environmental data, and how to build and train AI agents.
- Deep Learning. Methods And Applications 3 Free book from Microsoft Research
- Neural Networks And Deep Learning 3 – Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning
- Machine Learning: A Probabilistic Perspective – This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach
- Deep Learning 1 – Yoshua Bengio, Ian Goodfellow and Aaron Courville put together this currently free (and draft version) book on deep learning. The book is kept up-to-date and covers a wide range of topics in depth (up to and including sequence-to-sequence learning).
- Getting Started with Deep Learning and Python 6
- Machine Learning Mastery 3
- Deep Learning.net 3 – Aggregation site for DL resources
- Awesome Machine Learning 22 – Like this Github, but ML-focused
- FastML 3
- Awesome Deep Learning Resources 5 – Rough list of learning resources for Deep Learning
- Professional and In-Depth Machine Learning Video Courses 2 – A collection of free professional and in depth Machine Learning and Data Science video tutorials and courses
- Professional and In-Depth Artificial Intelligence Video Courses – A collection of free professional and in depth Artificial Intelligence video tutorials and courses
- Professional and In-Depth Deep Learning Video Courses 1 – A collection of free professional and in depth Deep Learning video tutorials and courses
- Introduction to Machine Learning 8 – Introductory level machine learning crash course
- Awesome Graph Classification – Learning from graph stuctured data
- Awesome Community Detection – Clustering graph structured data
- Awesome Decision Tree Papers – Decision tree papers from machine learning conferences
- Awesome Gradient Boosting Papers 1 – Gradient boosting papers from machine learning conferences
- Awesome Fraud Detection Papers – Fraud detection papers from machine learning conferences
- Awesome Neural Art 1 – Creating art and manipulating images using deep neural networks.
- IEEE Computational Intelligence Society 1
- Machine Intelligence Research Institute 1
- OpenAI 2
- Association For The Advancement of Artificial Intelligence
- Google DeepMind Research
- Nvidia Deep Learning
- AI Google 1
- Facebook AI 1
- AI & Society 3
- AI Communications
- AI Magazine 2
- Annals of Mathematics and Artifical Intelligence
- Applicable Algebra in Engineering, Communication and Computing
- Applied Artificial Intelligence
- Applied Intelligence
- Artificial Intelligence for Engineering Design, Analysis and Manufacturing 1
- Artificial Intelligence Review
- Artificial Intelligence
- Automated Software Engineering
- Autonomous Agents and Multi-Agent Systems 1
- Computational and Mathematical Organization Theory
- Computational Intelligence
- Electronic Transactions on Artificial Intelligence
- Evolutionary Intelligence
- EXPERT—IEEE Intelligent Systems
- IEEE Transactions Automation Science and Engineering 1
- Intelligent Industrial Systems 1
- International Journal of Intelligent Systems
- International Journal on Artificial Intelligence Tools
- Journal of Artificial Intelligence Research
- Journal of Automated Reasoning
- Journal of Experimental and Theoretical Artificial Intelligence
- Journal of Intelligent Information Systems
- Journal on Data Semantics
- Knowledge Engineering Review
- Minds and Machines
- Progress in Artificial Intelligence
- AI Digest 2. A weekly newsletter to keep up to date with AI, machine learning, and data science. Archive.
- Open Cognition Project 1 – We’re undertaking a serious effort to build a thinking machine
- AITopics 1 – Large aggregation of AI resources
- AIResources 2 – Directory of open source software and open access data for the AI research community
- Artificial Intelligence Subreddit 1
- AI Experiments with Google 2
To the extent possible under law, Owain Lewis has waived all copyright and related or neighboring rights to this work.
Source GitHub 7
Awesome Artificial Intelligence
A curated list of artificial intelligence resources (Courses, Tools, App, Open Source Project)
- Courses & Articles 1
- Artificial Intelligence Company & Reseach Institute 1
- Artificial Intelligence Tools 1
- Books 3
- Events and Conferences 1
Courses & Articles
- AI & ML Events – Discover the best upcoming hand-picked events in the field of artificial intelligence and machine learning
- Machine Learning 2 – Stanford University This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Taught by: Andrew Ng
- MIT Artifical Intelligence Videos 34 – MIT This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.
- Machine Learning 18 – Basic machine learning algorithms for supervised and unsupervised learning
- Deep Learning for Natural Language Processing – University of Oxford This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.
- Tensorflow for Deep Learning Research 1 – Stanford University This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow.
- Deep Learning for Natural Language Processing 1 – Stanford University Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc.
- Machine Learning 1 – Cornell University This course will introduce you to technologies for building data-centric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.
- Deep Learning Explained 1 – Microsoft This course provides the level of detail needed to enable engineers / data scientists / technology managers to develop an intuitive understanding of the key concepts behind this game changing technology.
- Machine Learning: Regression 1 – University of Washington In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,…). This is just one of the many places where regression can be applied.
- Machine Learning: Clustering & Retrieval – University of Washington A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?
- Neural Networks for Machine Learning 2 – University of Toronto with Geoffrey Hinton Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.
- Machine Learning With Big Data – University of California, San Diego Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems.
- Introduction to Artificial Intelligence 1 – UC Berkeley This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
- Advanced Artificial Intelligence – Cornell University The design of systems that are among top 10 performers in the world (human, computer, or hybrid human-computer).
- Artificial Intelligence (AI) 2 – Columbia University with Professor Ansaf Salleb-Aouissi This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
Generative Adversarial Networks (GANs)
- A Beginner’s Guide to Generative Adversarial Networks (GANs) 2 – Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other.
- GAN — What is Generative Adversary Networks GAN? – GAN is about creating, like drawing a portrait or composing a symphony. This is hard compared to other deep learning fields…
- Artificial Intelligence for Robotics 1 – Georgia Tech Artificial Intelligence for Robotics by Sebastian Thrun
- Advanced Robotics 1 – UC Berkeley The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization—two areas with wide applicability in modern Artificial Intelligence.
Artificial Intelligence Company & Research Institute
Business Intelligence & Analytics
- Arago/HIRO 1 — optimise and autonomously IT and business operations
- Arimo — solution to help predict customer activity and fraud
- Ayasdi — a suite of intelligent applications for enterprise
- DataRobot — a range of products to improve enterprise products
- Dataminr — discovers events and breaking information before the news
- Einstein 1 — a smarter Salesforce
- Fuzzy AI — adds intelligent decision making to web and mobile apps
- Logz.io — helps you index, search, visualise and analyse your data
- NXT AI — is a framework for temporal pattern recognition and prediction
- Paxata] — to transform raw data into useful information automatically
- Poweredby.ai — helps you monitor server bugs
- Sundown 1 — automates repetitive tasks within your business
- UBIX 1 — making complex data science easy for enterprise
- Geometric Intelligence 1 – Geometric Intelligence apart of the Uber AI Labs
- kaggle – a platform for predictive modelling and analytics competitions in which companies and researchers post data and statisticians and data miners compete to produce the best models for predicting and describing the data
- Boston Dynamics 1 – an engineering and robotics design company that is best known for the development of BigDog
- iRobot – manufacturer of the famous robotic vacuum cleaner
- DJI – industry leader in drones for both commerical and industrial needs.
- Fetch Robotics – The future of e-commerce fulfillment and R&D robots.
- ABB Robotics 1 – the largest manufacturer of industrial robots
- Aldebaran Robotics – creator of the [NAO robot
- FANUC 1 – industrial robots manufacturer with the biggest install base
- Rethink Robotics – creator of the collaborative robot [Baxter]
Conversational Interfaces & Chatbots
- API.ai — advanced tools needed to build conversational user interfaces
- Chatfuel — build a Facebook chatbot without coding
- Comm.ai — add voice and chat API to websites and apps
- Conversica — conversational interfaces to help get more sales
- EDDI — create, test and deploy chatbots
- FPT AI Platform — automated interaction with end-users
- Golem.ai — natural language interpretation tool for developers
- Gong — analyses and improves sales conversations and discovery calls
- Kasisto — conversational AI platform for the finance industry
- KITT.AI — create conversational agents using a visual interface
- Maluuba — teaching machines to think, reason and communicate
- Massively — build chatbots for business
- Meya — build, train and host bots in one platform
- MindMeld — improved version of Siri
- Motion AI — chat bots made easy
- msg.ai — chatbot with management dashboard
- Octane AI — marketing automation for messaging
- OpenAI Gym — open source interface to reinforcement learning tasks
- Orbit — tools to help to help automate conversational AI
- Pool — personal assistant that helps you get more work done
- Recast — collaborative platform to build, train, deploy intelligent bots
- Reply.ai — platform to build and manage your conversational strategy
- Semantic Machines — conversational AI for work, travel, shop and play
- Snips — add a voice Assistant to your connected product
- Servo 1 — full spectrum bot and voice which integrates with existing systems
- UNU.ai — using the Swarm Intelligence (group brainpower) for chatbots
- Unify — e-commerce chatbot
- uTu — multi-channel bot analytics and data management
- Wechaty – Wechaty is a Bot Framework for Wechat Personal Account which can help you create a bot
- Wit.ai — easily create text or voice based bots for preferred platform
- Wysh — enterprise scale chatbot with payment methods
- Zero AI — voice interface that understands meaning, intent and context
- BigML 1 — single platform for all predictive use cases
- CrowdFlower — helps with sentiment analysis, search relevance, and more
- Dataiku — data science platform for prototype, deploy, and run at scale
- DataScience — enterprise data science platform for R&D and production
- Domino Data Lab — platform for collaborating, building and deploying
- Kaggle — helps you learn, work, and play with machine learning models
- RapidMiner — makes data science teams more productive
- Seldon — helps DS teams put machine learning models into production
- SherlockML — a platform to build, test, and deploy AI algorithms
- Spark — research engine, capable of discovering complex patterns in data
- Tamr — makes data unification of data silos possible
- Trifacta — helps put data into useful structures for analysis
- Yhat — allows data scientists to deploy and update predictive models rapidly
- Yseop — automate the writing of reports, websites, emails, articles and more
- AnOdot — detects business incidents
- Bonsai — develop more adaptive, trusted and programmable AI models
- Deckard.ai — helps predict project timelines
- Fuzzy.ai — adds intelligent decision making to web and mobile apps
- IBM Watson — AI platform for business
- Gigster — connecting projects with the right team
- Kite 1 — augments your coding environment with web available knowledge
- Layer 6 AI — deep learning platform for prediction and personalisation
- Morph — makes developing chatbots for your business easy
- Ozz — make your bot smarter, by helping it self learn
- RainforestQA — rapidly web and mobile app testing
- SignifAI — increase server uptime and predict downtime
- Turtle — project management and chat software that’s easy for teams
- Neural Network – Libraries by Sony. Sony demonstrates its interest in deep learning by releasing their own open source deep learning framework.
- TensorFlow neural network playground – Play with neural networks visually in your browser to get a feel for what they are and what they do.
- Vinli — turns any car into a smart car
- Apollo 1 – by Baidu. Newly launched source platform for building autonomous vehicles.
Insurance / Legal
Artificial Intelligence Tools
- Amazon Echo / Alexa — everyday personal assistant for in-home
- Apple Siri — everyday personal assistant on iPhone and Mac
- Brin — helps you make smarter business decisions
- Chatfuel — create a Facebook chatbot in 7 minutes
- Findo — smart search assistant across email, files and personal cloud.
- Fembot 4 — your AI girlfriend
- Fin — a powerful personal assistant
- Focus 1 — helps you focus, get tasks done and prioritise your day
- Gatebox 1 — a holographic anime assistant in an espresso machine
- Google Assistant 1 — everyday personal assistant
- Howdy – a friendly, trainable bot that helps Slack teams with work
- Hound — everyday personal assistant
- Julie Desk 1 — meeting scheduling assistant (aimed at C-Suite)
- Kono — meeting scheduling assistant
- Lifos — dynamic independent entities that interact with the web and social
- Ling — similar to Amazon Echo
- Luka — chatbot messenger for people and other chatbots
- Lyra — monitor analyse your carbon emissions
- Magic – Magic is a special phone number you text to get anything you want, hassle free
- Microsoft Cortana – Cortana is a voice-controlled virtual assistant for Microsoft Windows Phone 8.1. Comparable to Siri, the intelligent assistant enabled on Apple devices, Microsoft’s Cortana will use the Bing search engine and data stored on the user’s smartphone by to make personalized recommendations
- MyWave 1 – Melbourne-based which makes a personal call
- Meeco – Sydney-based, a robot lawyer
- Mimetic — meeting scheduling assistant
- My Ally — handles meeting scheduling and manages calendar
- Mycroft 1 — is the world’s first open source voice assistant
- myWave — chatbot to help you throughout your daily life
- Remi— like Siri with an interface
- Replika— your AI friend that you raise through text conversations
- SkipFlag — automatically discover and organise your work
- Spoken — virtual assistant with an interface
- Vesper — virtual assistant aimed at C-Suite
- Viv 1 — like Siri but 10x better
- x.ai — x.ai is a personal assistant who schedules meetings for you
- Zoom.ai — personal assistant to help you at work
- Thirdleap — helps children to learn maths
- Woogie — the conversational AI robot that makes learning and discovery fun for children
- XiaoJing Bot – XiaoJing Bot to support management of wechat groups and remove members of wechat group
Health / Medical Tools
- Abi — your virtual health assistant
- Ada 1 — can help if you’re feeling unwell
- Airi — personal health coach
- Alz.ai — helps you care for loved ones with Alzheimer’s
- Bitesnap — food recognition from meal photos to help count calories
- doc.ai — makes lab results easy to understand
- Gyan — helps you go from symptoms to likely conditions
- Joy — helps you track and improve your mental health
- Kiwi — helps you to reduce and quit smoking
- Tess by X2AI — therapist in your pocket
- Sleep.ai — diagnose snoring and tooth grinding
Travel AI Tools
- Ada — chatbot that helps you navigate and make decisions
- Emma — automatically calculates and adds meeting travel time
- ETA — helps you manage travel itineraries and meetings
- HelloGbye — book complex trips with simple speech
- Mezi —helps with booking flights, hotels, restaurant reservations and more
- Nexar — dash cam app that helps you drive safer
- Ready — traffic forecaster and travel time prediction
- Spatial 1 — reveal the social layer of cities
Finance AI Tools
- Abe 1 — fast answers about your finances
- Andy — a personal Tax Accountant
- Ara — helps you budget
- Bond — helps you achieve your financial goals
- Mylo — rounds up your everyday purchases and invest the spare change
- Olivia — helps you manage your finances
- Responsive — institutional-grade active portfolio management
- Roger — helps you pay bills easily
- Xoe.ai — AI lending chatbot
Language / Translation AI Tools
- Microsoft Translator — language translator powered by neural networks
- Watson.ai 1 — legal, academic and financial translations
IoT / IIoT
- Aerial — home activity, movement and identity sensor
- Bridge.ai 1 — smart-home platform focused on speech and sound
- Cubic — one place to connect your smart home devices
- Grojo — grow room controller and monitoring system
- Home — autonomous home operations with connected devices
- Hello — helps you monitor and improve your sleep
- Josh — whole house voice control
- Mycroft 1 — is the world’s first open source voice assistant
- Nanit — baby monitor that measures sleep and caregiver interactions
- Nest — a range of in-home devices such as Thermostat, security and alarms
- Apollo 1 — breaks down articles and PDF’s into quick, readable dot points
- Ferret.ai — helps you research by summarising articles and search ability
- Iris — helps you research and visualise concepts in research papers
- 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.
Blogs, Papers, and Articles
- Deep learning reading list – A thorough list of academic survey papers on the subjects of reinforcement learning, computer vision, NLP & speech, disentangling factors, transfer learning, practical tricks, sparse coding, foundation theory, feedforward networks, large scale deep learning, recurrent networks, hyper parameters, optimization, and unsupervised feature learning.
- [Deep Learning in a Nutshell] – (https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/) – by Tim Dettmers, via NVidia (2015). These articles are digestible and do not rely heavily on math. There are 3 parts: Part 1(A gentle introduction to deep learning that covers core concepts and vocabulary). Part2 ( History of deep learning and methods of training deep learning architectures quickly and efficiently) Part 3 (Sequence learning with a focus on natural language processing)
- TensorFlow – Large-Scale Machine Learning on Heterogeneous Distributed Systems by Google Research (2015). How TensorFlow works.
- Caffe – Deep learning framework.
- Alexa Skill Kit – Library for effortless Alexa Skill development with AWS Lambda
- Facebook Messenger chatbot boilerplate – PHP Facebook Messenger chatbot boilerplate
- Facebook Messenger wit.ai node.js boilerplate -Facebook Messenger wit.ai node.js boilerplate
- Telegram Bot API PHP SDK 1 – Telegram Bot API PHP SDK. Supports Laravel out of the box
- Wechaty – Wechaty is a Bot Framework for Wechat Personal Account which can help you create a bot
- Node.js Messenger Bot – A Node client for the Facebook Messenger Platform
- BootBot – Facebook Messenger Bot Framework for Node.js
- Ruby Telegram bot boilerplate
- python-telegram-bot 1 – This library provides a pure Python interface for the Telegram Bot API
- Twitter-text – Twitter’s text processing library
- natural 1 – General natural language facilities for node
- Clustering.js – Clustering algorithms implemented for Node.js and the browser
- Kmeans.js – Implementation of the k-means algorithm, for node.js and the browser
- DN2A – Digital Neural Networks Architecture
- Knwl.js – A Natural Language Processor in JS
- NLP Compromise – Natural Language processing in the browser
- Machine Learning – Machine learning library for Node.js
- machineJS – Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration.
- Node-fann – FANN (Fast Artificial Neural Network Library) bindings for Node.js
- brain.js – Neural Networks
- Synaptic – Neural Networks
- Natural 1 – Natural Language Processing
- ConvNetJS – Convolutional Neural Networks
- mljs – A set of sub-libraries with a variety of functions
- Neataptic – Neural Networks
- Webdnn – Deep Learning
- Lasagne 1 – Lightweight Python library for deep learning (built on Theano).
- AI Weekly 2 — a weekly collection news and resources on AI and ML
- Approximately Correct — AI and Machine Learning blog
- Axiomzen — AI newsletter delivered every 2 weeks
- Concerning.ai — AI commentators
- Fast.ai 1 — dedicated to making the power of deep learning accessible to all
- Machinelearning.ai 1 — dedicated news and updates for ML and AI
- Machine Learning Weekly 2 — a hand-curated newsletter ML and DL
- Artificial Intelligence News – ScienceDaily 1 -Artificial Intelligence News. Everything on AI including futuristic robots with artificial intelligence, computer models of human intelligence and more.
- Podcast with Yoshua Bengio 1 – The Rise of Neural Networks and Deep Learning in Our Everyday Lives. An exciting overview of the power of neural networks as well as their current influence and future potential.
Events and Conferences
- The AI Conference — an annual event where leading AI researchers and top industry practitioners meet and collaborate
- The AI Forum — Montreal based AI conference
- Artificial Intelligence Conference — Bootstrap Labs Venture firm
- Events.ai — the one stop shop for AI/ML/DL events and conferences
- Nucl.ai — game AI conference and courses
- Chatbot Summit – Chatbot Summit Berlin is the second international Chatbot Summit destined to bring together the leading players of the newly formed Chatbot economy
- Deep learning Google Group – Where deep learning enthusiasts and researchers hangout and share latest news.
- Deep learning research groups – A list of many of the academic and industry labs focused on deep learning.
- Amsterdam — AI community and events
- Berlin — AI community and events
- Beijing – AI community and events
- Brisbane – AI community and events
- Hamburg — AI community and events
- Hongkong — AI community and events
- London — AI community and events
- Madrid — AI community and events
- Melbourne – AI community and events
- Milan — AI community and events
- New York — AI community and events
- Oslo — AI community and events
- San Francisco AI meetup – A local meetup for AI enthusiasts and researchers that we’re involved in.
- Seattle — AI community and events
- Shanghai — AI community and events
- Shenzhen — AI community and events
- Singapore — AI community and events
- Stockholm — AI community and events
- Sydney – AI community and events
- Chatbots NYC 1 – Meetup in New York City
- Viv — like Siri but 10x better
- x.ai— meeting scheduling assistant
- Zoom.ai — personal assistant to help you at work
Source GitHub 12
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.
Artificial Intelligence Resources
Artificial Intelligence is advancing in an incredible fast pace and staying up to date with the state-of-the-art research is, sometimes, overwhelming. This repository is my “reading list”, a collection of interesting papers, courses, blogs, videos and other resources related to Machine Learning and Cognitive Systems in general.
An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning
Awesome Machine Learning
A curated list of awesome Machine Learning frameworks, libraries and software.