Artificial Intelligence Collection

Welcome To The World of AI Collection ®. A collection of awesome software, libraries, Learning Tutorials, documents, books, resources and interesting stuff about Artificial Intelligence. Thanks to our daily readers and contributors. The goal is to build a categorized community-driven collection of very well-known resources. Sharing, suggestions and contributions are always welcome!

What is Artificial Intelligence?

Some general definitions

• “Artificial intelligence is a computerised system that exhibits behaviour that is commonly thought of as requiring intelligence.” (1)

• “Artificial Intelligence is the science of making machines do things that would require intelligence if done by man.” (2)

The founding father of AI, Alan Turing, defines this discipline as:

• “AI is the science and engineering of making intelligent machines, especially intelligent computer programs.” (3)

Table of Contents

1. Courses
2. Books
3. Programming
4. Philosophy
5. Free Content
6. Code
7. Videos
8. Learning
9. Organizations
10. Journals
11. Competitions
12. Newsletters
13. Misc
14. Bookmarks

Courses

• 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 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 - A free deep reinforcement learning course by OpenAI
• MIT Artifical 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 the world of 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
  • Also consider browsing the list of recommended reading, divided by each chapter in "Artificial Intelligence: A Modern Approach".
• 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 itroduces the ML concepts & strategies with lots of practical usages.
• 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-threating way.
• MachineLearningWithTensorFlow2ed - 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.
• 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.

Programming

• Prolog Programming For Artificial Intelligence - 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 - PDF here
• Python Tools for Machine Learning
• Python for Artificial Intelligence

Philosophy

• Super Intelligence - 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 - 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 - 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

• Foundations Of Computational Agents - 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 - This course provides a broad introduction to machine learning and statistical pattern recognition.
• Computers and Thought: A practical Introduction to Artificial Intelligence - 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 - 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 - 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 - 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.
• Modeling Agents with Probabilistic Programs - This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning.

Code

• ExplainX- ExplainX is a fast, light-weight, 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, 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

• A tutorial on Deep Learning
• Basics of Computational Reinforcement Learning
• Deep Reinforcement Learning
• Intelligent agents and paradigms for AI
• 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- 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-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.

Learning

• Deep Learning. Methods And Applications Free book from Microsoft Research
• Neural Networks And Deep Learning - 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 - 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
• Machine Learning Mastery
• Deep Learning.net - Aggregation site for DL resources
• Awesome Machine Learning - Like this Github, but ML-focused
• FastML
• Awesome Deep Learning Resources - Rough list of learning resources for Deep Learning
• Professional and In-Depth Machine Learning Video Courses - 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 - A collection of free professional and in depth Deep Learning video tutorials and courses
• Introduction to Machine Learning - 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 - Gradient boosting papers from machine learning conferences
• Awesome Fraud Detection Papers - Fraud detection papers from machine learning conferences
• Awesome Neural Art - Creating art and manipulating images using deep neural networks.

Organizations

• IEEE Computational Intelligence Society
• Machine Intelligence Research Institute
• OpenAI
• Association For The Advancement of Artificial Intelligence
• Google DeepMind Research
• Nvidia Deep Learning
• AI Google
• Facebook AI
• IBM Research
• Microsoft Research

Journals

• AI & Society
• AI Communications
• AI Magazine
• 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
• Artificial Intelligence Review
• Artificial Intelligence
• Automated Software Engineering
• Autonomous Agents and Multi-Agent Systems
• Computational and Mathematical Organization Theory
• Computational Intelligence
• Electronic Transactions on Artificial Intelligence
• Evolutionary Intelligence
• EXPERT—IEEE Intelligent Systems
• IEEE Transactions Automation Science and Engineering
• Intelligent Industrial Systems
• 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

Competitions

• MIT Battlecode
• AI Challenge
• AI Games

Newsletters

• AI Digest. A weekly newsletter to keep up to date with AI, machine learning, and data science. Archive.

Misc

• Open Cognition Project - We're undertaking a serious effort to build a thinking machine
• AITopics - Large aggregation of AI resources
• AIResources - Directory of open source software and open access data for the AI research community
• Artificial Intelligence Subreddit
• AI Experiments with Google

Bookmarks

2018-2019

• How to use transfer learning and fine-tuning in Keras and Tensorflow to build an image recognition…
• How to deploy Machine Learning models with TensorFlow. Part 1 — make your model ready for serving.
• image-classification-indoors-outdoors/image-classification.ipynb at master · manena/image-classification-indoors-outdoors
• (620) Learning to Communicate with Deep Multi-Agent Reinforcement Learning - Jakob Foerster - YouTube
• Compressing deep neural nets
• Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks - Uber Engineering Blog
• Run python script from init.d
• Daemon vs Upstart for python script - Stack Overflow
• Reinforcement learning for complex goals, using TensorFlow - O'Reilly Media
• Blockchains: How They Work and Why They’ll Change the World - IEEE Spectrum
• NET292.profile.indd
• GANs are Broken in More than One Way: The Numerics of GANs
• (74) Stanford Seminar - "Deep Learning for Dummies" Carey Nachenberg of Symantec and UCLA CS - YouTube
• Fast.ai: What I Learned from Lessons 1–3 – Hacker Noon
• Meet Horovod: Uber's Open Source Distributed Deep Learning Framework
• Home · cat /var/log/life
• 2D & 3D Visualization using NCE Cost | Kaggle
• New Theory Cracks Open the Black Box of Deep Learning | Quanta Magazine
• Feature Visualization
• Face It – The Artificially Intelligent Hairstylist | Intel® Software
• What is TensorFlow? | Opensource.com
• Estimating an Optimal Learning Rate For a Deep Neural Network – Medium
• Understanding Hinton’s Capsule Networks. Part I: Intuition.
• Capsule Networks Are Shaking up AI — Here’s How to Use Them
• Research Blog: Eager Execution: An imperative, define-by-run interface to TensorFlow
• Google and Uber’s Best Practices for Deep Learning – Intuition Machine – Medium
• TFX: A TensorFlow-based production scale machine learning platform | the morning paper
• Comprehensive data exploration with Python | Kaggle
• An Easy Guide to build new TensorFlow Datasets and Estimator with Keras Model | DLology
• Distributed TensorFlow: A Gentle Introduction
• Google Developers Blog: Introduction to TensorFlow Datasets and Estimators
• Google Developers Blog: Introducing TensorFlow Feature Columns
• TensorLy: Tensor learning in Python
• Question answering with TensorFlow - O'Reilly Media
• Kubernetes + GPUs 💙 Tensorflow – Intuition Machine – Medium
• Welcoming the Era of Deep Neuroevolution - Uber Engineering Blog
• Deep Learning for NLP, advancements and trends in 2017 - Tryolabs Blog
• Turning Design Mockups Into Code With Deep Learning - FloydHub Blog
• AI and Deep Learning in 2017 – A Year in Review – WildML
• Research Blog: The Google Brain Team — Looking Back on 2017 (Part 1 of 2)
• Reinforcement Learning · Artificial Inteligence
• Sketching Interfaces – Airbnb Design
• Machine Learning Explained: Understanding Supervised, Unsupervised, and Reinforcement Learning - Data Science Central
• Fine-tuning Convolutional Neural Network on own data using Keras Tensorflow – CV-Tricks.com
• A neural approach to relational reasoning | DeepMind
• Deep Reinforcement Learning Doesn't Work Yet
• Big Picture: Google Visualization Research
• Research Blog: Using Evolutionary AutoML to Discover Neural Network Architectures
• Secure Computations as Dataflow Programs - Cryptography and Machine Learning
• Teach Machine to Comprehend Text and Answer Question with Tensorflow - Part I · Han Xiao Tech Blog
• Deep Reinforcement Learning: Pong from Pixels
• Tensorboard on gcloud
• Entity extraction using Deep Learning based on Guillaume Genthial work on NER
• Deep Learning Book Notes, Chapter 3 (part 1): Introduction to Probability
• Predicting physical activity based on smartphone sensor data using CNN + LSTM
• Learn Word2Vec by implementing it in tensorflow – Towards Data Science
• TutorialBank: Learning NLP Made Easier - Alexander R. Fabbri
• How to Quickly Train a Text-Generating Neural Network for Free
• Code2Pix - Deep Learning Compiler for Graphical User Interfaces
• naacl18.pdf
• Deep Learning for Object Detection: A Comprehensive Review
• 4 Sequence Encoding Blocks You Must Know Besides RNN/LSTM in Tensorflow · Han Xiao Tech Blog - Deep Learning, Tensorflow, Machine Learning and more!
• Automated front-end development using deep learning
• A New Angle on L2 Regularization
• Another Datum
• IML-Sequence
• ml4a-guides/q_learning.ipynb at experimental · ml4a/ml4a-guides
• tensorflow-without-a-phd/00_RNN_predictions_playground.ipynb at master · GoogleCloudPlatform/tensorflow-without-a-phd
• Convolutional Neural Network based Image Colorization using OpenCV | Learn OpenCV
• Transfer Learning in NLP – Feedly Blog
• CS 229 - Deep Learning Cheatsheet
• Google AI Blog: Introducing a New Framework for Flexible and Reproducible Reinforcement Learning Research
• Building a text classification model with TensorFlow Hub and Estimators
• Deploy TensorFlow models – Towards Data Science
• Deep Learning – Mohit Jain
• Анализ тональности текстов с помощью сверточных нейронных сетей / Блог компании Mail.Ru Group / Хабр
• Machine Reading Comprehension Part II: Learning to Ask & Answer · Han Xiao Tech Blog - Deep Learning, Tensorflow, Machine Learning and more!
• How to Quickly Train a Text-Generating Neural Network for Free
• Auto-Keras, or How You can Create a Deep Learning Model in 4 Lines of Code
• More Effective Transfer Learning for NLP
• Machine Learning using Google Cloud ML Engine. – Gautam Karmakar – Medium
• Training and Serving ML models with tf.keras – TensorFlow – Medium
• How to deploy TensorFlow models to production using TF Serving
• Playing Mortal Kombat with TensorFlow.js. Transfer learning and data augmentation · Minko Gechev's blog
• Beyond Interactive: Notebook Innovation at Netflix – Netflix TechBlog – Medium
• Mask R-CNN with OpenCV - PyImageSearch
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time
• Serving ML Quickly with TensorFlow Serving and Docker
• Human-Centered AI
• Keras as a simplified interface to TensorFlow: tutorial
• Serving Google BERT in Production using Tensorflow and ZeroMQ · Han Xiao Tech Blog - Deep Learning, NLP, AI
• Multilingual Sentence Embeddings for Zero-Shot Transfer – Applying a Single Model on 93 Languages | Lyrn.AI
• Deploy flask app with nginx using gunicorn and supervisor
• Dept. of Computer Sci.: Module Handbook for the Bachelor and Master Programmes
• 14 NLP Research Breakthroughs You Can Apply To Your Business
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) – Jay Alammar – Visualizing machine learning one concept at a time
• A gallery of interesting Jupyter Notebooks · jupyter/jupyter Wiki
• CS294-158 Deep Unsupervised Learning Spring 2018
• Object Detection in Google Colab with Custom Dataset
• Advanced Visualization for Data Scientists with Matplotlib
• FavioVazquez/ds-cheatsheets: List of Data Science Cheatsheets to rule the world
• Gentle Dive into Math Behind Convolutional Neural Networks
• Customer churn prediction in telecom using machine learning in big data platform
• How to Port-Forward Jupyter Notebooks – Scott Hawley – Development Blog
• Top 8 trends from ICLR 2019
• The Illustrated Word2vec – Jay Alammar – Visualizing machine learning one concept at a time
• Google AI Blog: Transformer-XL: Unleashing the Potential of Attention Models
• TensorFlow & reflective tape : why I’m bad at basketball 🏀
• Topic Modeling with LSA, PLSA, LDA & lda2Vec
• GAN — Some cool applications of GANs. – Jonathan Hui – Medium
• A Recipe for Training Neural Networks
• Practice Quantum Computing | Brilliant
• dennybritz/reinforcement-learning: Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
• Weight Agnostic Neural Networks
• Transformers from scratch | Peter Bloem
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time
• The Illustrated GPT-2 (Visualizing Transformer Language Models) – Jay Alammar – Visualizing machine learning one concept at a time
• ml-dl -
• Indaba2019 NLP Talk.pdf - Google Drive
• Automation via Reinforcement Learning
• CS 224N | Home
• mihail911/nlp-library: curated collection of papers for the nlp practitioner 📖👩‍🔬
• Production-ready Docker images
• The key lessons from “Where Good Ideas Come From” by Steven Johnson
• Neural Networks Example, Math and code – Brian Omondi Asimba
• How to apply machine learning and deep learning methods to audio analysis
• A Visual Guide to Using BERT for the First Time – Jay Alammar – Visualizing machine learning one concept at a time
• NeurIPS · SlidesLive
• https://towardsdatascience.com/from-pre-trained-word-embeddings-to-pre-trained-language-models-focus-on-bert-343815627598
• Joel Grus – Fizz Buzz in Tensorflow
• (160) Visual Interpretability of CNNs | Himanshu Rawlani | PyData Pune Meetup | July 2019 - YouTube
• Memo's Island: A simple and interpretable performance measure for a binary classifier
• Data-Science-Periodic-Table.pdf
• Writing a Generic TensorFlow Serving Client for Tensorflow Serving models
• Writing a Generic TensorFlow Serving Client for Tensorflow Serving models
• dspace.mit.edu/bitstream/handle/1721.1/41487/AI_WP_316.pdf
• Transformers are Graph Neural Networks | NTU Graph Deep Learning Lab
• 7 advanced pandas tricks for data science - Towards Data Science
• Google AI Blog: XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization
• CNN Explainer
• Polo Club of Data Science @ Georgia Tech: Human-Centered AI, Deep Learning Interpretation & Visualization, Cybersecurity, Large Graph Visualization and Mining | Georgia Tech | Atlanta, GA 30332, United States
• Sara Robinson
• Common statistical tests are linear models (or: how to teach stats)
• Zero-Shot Learning for Text Classification

2015 - 2018

• Python Deep Learning Projects
• Deep Learning
• Fast Artificial Neural Network Library (FANN)
• The Nature of Code
• Create and Train Custom Neural Network Architectures - MATLAB & Simulink - MathWorks India
• limdu js framework
• Neural networks and deep learning
• NN Why Does it Work?
• Introduction to Convex Optimization | Electrical Engineering and Computer Science | MIT OpenCourseWare
• Python Programming Tutorials imge recognition
• Data Science and Machine Learning Essentials | edX
• Deep learning – Convolutional neural networks and feature extraction with Python | Pyevolve
• 50 external machine learning / data science resources and articles - Data Science Central
• Hacker's guide to Neural Networks
• Fast Forward Labs: How do neural networks learn?
• Machine Learning
• Memkite – Deep Learning for iOS (tested on iPhone 6S), tvOS and OS X developed in Metal and Swift | Memkite
• Demis Hassabis, CEO, DeepMind Technologies - The Theory of Everything | Machine Learning & Computer Vision Talks
• DataTau- hacker news on DL
• Deeplearning4j - Open-source, distributed deep learning for the JVM
• Torch | Recurrent Model of Visual Attention
• Machine Learning for Developers by Mike de Waard
• Deep Learning - Community - Google+
• A Tour of Machine Learning Algorithms - Data Science Central
• Understanding Natural Language with Deep Neural Networks Using Torch | Parallel Forall
• What a Deep Neural Network thinks about your #selfie
• Jason Yosinski
• WildML | A blog about Machine Learning, Deep Learning and NLP.
• Getting Started — TensorFlow
• Deep Learning:Theoretical Motivations - VideoLectures.NET
• Unsupervised Feature Learning and Deep Learning Tutorial
• Wit — Getting Started
• research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
• ujjwalkarn/Machine-Learning-Tutorials
• Top 10 Machine Learning APIs: AT&T Speech, IBM Watson, Google Prediction | ProgrammableWeb
• NeuroVis | An interactive introduction to neural networks
• learning_tensorflow/word2vec.md at master · chetannaik/learning_tensorflow
• intro2deeplearning/notebooks at master · rouseguy/intro2deeplearning
• Autoencoders - Ep. 10 (Deep Learning SIMPLIFIED) - YouTube
• Python Programming Tutorials
• How to Prepare Data For Machine Learning - Machine Learning Mastery
• Solve Machine Learning Problems Step­-by­-Step - Machine Learning Mastery
• Implementing a CNN for Text Classification in TensorFlow – WildML
• Anyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN) - i am trask
• 7 Steps to Mastering Machine Learning With Python
• DeepLearningKit – Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS | Open Source Deep Learning Framework for iOS, OS X and tvOS
• A Visual Introduction to Machine Learning
• Attention and Memory in Deep Learning and NLP – WildML
• A Neural Network in 11 lines of Python (Part 1) - i am trask
• Python Training | Python For Data Science | Learn Python
• Understanding LSTM Networks -- colah's blog
• deeplearning4nlp-tutorial/2015-10_Lecture at master · nreimers/deeplearning4nlp-tutorial
• Collection Of 51 Free eBooks On Python Programming
• Analyzing 50k fonts using deep neural networks | Erik Bernhardsson
• Data Science Ontology
• Reddit Machine Learning
• RNNs in Darknet
• caesar0301/awesome-public-datasets: An awesome list of high-quality open datasets in public domains (on-going).
• A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, distributed deep learning for the JVM
• Essentials of Machine Learning Algorithms (with Python and R Codes)
• PythonForArtificialIntelligence - Python Wiki
• carpedm20/lstm-char-cnn-tensorflow: LSTM language model with CNN over characters in TensorFlow
• kjw0612/awesome-rnn: Recurrent Neural Network - A curated list of resources dedicated to RNN
• sherjilozair/char-rnn-tensorflow: Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow
• Stanford University CS231n: Convolutional Neural Networks for Visual Recognition
• Top Youtube Videos On Machine Learning, Neural Network & Deep Learning
• The Spectator ← Shakir's Machine Learning Blog
• Preprocessing text data — Computational Statistics in Python 0.1 documentation
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• Next Big Future: Recurrent Neural Nets
• Must Know Tips/Tricks in Deep Neural Networks - Data Science Central
• Visual Question Answering Demo in Python Notebook – Aaditya Prakash (Adi) – Random Musings of Computer Vision grad student
• A Neural Network Playground
• Machine Learning : Few rarely shared trade secrets - Data Science Central
• Russell Stewart- debug NN
• Extracting meaningful content from raw HTML – Thomas Uhrig
• Russell Stewart
• Recurrent Neural Networks | The Shape of Data
• ITP-NYU - Spring 2016
• White Rain Noise Generator | White Noise & Rain Combined
• Machine Learning
• A GloVe implementation in Python - foldl
• Understanding Convolution in Deep Learning - Tim Dettmers
• The Chars74K image dataset - Character Recognition in Natural Images
• A Statistical View of Deep Learning (IV): Recurrent Nets and Dynamical Systems ← The Spectator
• Tensorflow and deep learning - without a PhD - Google Slides
• Parity problem, sequential: 1 bit at a time
• Machine learning with Python: A Tutorial
• Neural networks and deep learning
• Juergen Schmidhuber's home page - Universal Artificial Intelligence - New AI - Deep Learning - Recurrent Neural Networks - Computer Vision - Object Detection - Image segmentation - Goedel Machine - Theory of everything - Algorithmic theory of everything -
• t-SNE – Laurens van der Maaten
• Stanford University CS224d: Deep Learning for Natural Language Processing
• Machine Learning 10-701/15-781: Lectures
• Word2vec Tutorial | RaRe Technologies
• Machine learning |
• How to read: Character level deep learning – Offbit
• Generative Models
• goodrahstar/python-machine-learning-book: The "Python Machine Learning" book code repository and info resource
• A noob’s guide to implementing RNN-LSTM using Tensorflow — Medium
• Structuring Your TensorFlow Models
• Would You Survive the Titanic? A Guide to Machine Learning in Python - SocialCops Blog
• Berkeley AI Materials
• Hello, TensorFlow! - O'Reilly Media
• Visualize Algorithms based on the Backpropagation — NeuPy
• Talking Machines
• Probability Cheatsheet
• A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – CS Undergrad at UCLA ('19)
• Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur | No Free Hunch
• MNE — MNE 0.12.0 documentation
• Alexandre Gramfort - Telecom ParisTech
• Image Kernels explained visually
• Introduction to Recurrent Networks in TensorFlow
• The Ultimate List of TensorFlow Resources: Books, Tutorials & More - Hacker Lists
• the morning paper | an interesting/influential/important paper from the world of CS every weekday morning, as selected by Adrian Colyer
• The Ultimate List of TensorFlow Resources: Books, Tutorials & More - Hacker Lists
• ChristosChristofidis/awesome-deep-learning: A curated list of awesome Deep Learning tutorials, projects and communities.
• Nervana's Deep Learning Course - Nervana
• CNN practical
• What my deep model doesn't know... | Yarin Gal - Blog | Cambridge Machine Learning Group
• TensorFlow Linear Model Tutorial
• tensorflow/models · GitHub
• An introduction to Generative Adversarial Networks (with code in TensorFlow) - AYLIEN
• Neural Network Evolution Playground with Backprop NEAT | 大トロ
• Teaching an AI to write Python code with Python code • Will cars dream?
• GitHub - paarthneekhara/text-to-image: Tensorflow implementation of text to image synthesis using thought vectors
• Learning TensorFlow
• The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3) – Adit Deshpande – CS Undergrad at UCLA ('19)
• What is the Role of the Activation Function in a Neural Network?
• Research paper categorization using machine learning and NLP
• WaveNet: A Generative Model for Raw Audio | DeepMind
• Backpropagation In Convolutional Neural Networks - DeepGrid
• Urban Sound Classification
• Model evaluation, model selection, and algorithm selection in machine learning - Part II
• Data - Melbourne University AES/MathWorks/NIH Seizure Prediction | Kaggle
• cchio/deep-pwning: Metasploit for machine learning.
• First Contact With TensorFlow | Professor Jordi Torres | UPC & BSC-CNS | Barcelona
• Industry // AETROS
• alrojo/tensorflow-tutorial · GitHub
• Sequence prediction using recurrent neural networks(LSTM) with TensorFlow — Mourad Mourafiq
• Python Programming Tutorials
• Natural Language Processing and Voice Recognition Resources – Niaw de Leon
• Deep Learning for Beginners
• TensorFlow on Android - O'Reilly Media
• TensorFlow for Mobile Poets « Pete Warden's blog
• 5 algorithms to train a neural network | Neural Designer
• Hello DeepQ — koaning.io
• How do Convolutional Neural Networks work?
• Neural Network Architectures
• Question-Answer Dataset
• Image-to-Image Translation with Conditional Adversarial Networks
• GitXiv: Collaborative Open Computer Science
• Deep Learning Cheat Sheet
• Introduction to Recurrent Networks in TensorFlow
• DmitryUlyanov/neural-style-audio-tf · GitHub
• goodrahstar/tensorflow-value-iteration-networks: TensorFlow implementation of the Value Iteration Networks (NIPS '16) paper
• Recurrent Neural Network Tutorial for Artists | 大トロ
• Eric Jang: Summary of NIPS 2016
• GitHub - thtrieu/essence: AutoDiff DAG builder, built from scratch on top of numpy and C
• Deep Text Correcter
• GitHub - zhongwen/predictron: Tensorflow implementation of "The Predictron: End-To-End Learning and Planning"
• The major advancements in Deep Learning in 2016 - Tryolabs Blog
• Contact Me – the data science blog
• Wikipedia Monolingual Corpora | linguatools
• Tensorflow RNN-LSTM implementation to count number of set bits in a binary string
• buriburisuri/speech-to-text-wavenet: Speech-to-Text-WaveNet : End-to-end sentence level English speech recognition based on DeepMind's WaveNet and tensorflow
• AudioSet
• The amazing power of word vectors | the morning paper
• Let’s Make A Bot That Applies To Jobs For Us With Python – Millennial Dave
• goodrahstar/pytorch-tutorial: tutorial for researchers to learn deep learning with pytorch.
• Overview - seq2seq
• Baidu Deep Voice explained: Part 1 — the Inference Pipeline
• Tensorflow demystified – gk_ – Medium
• Transfer Learning - Machine Learning's Next Frontier
• Deep Learning with Emojis (not Math) – tech-at-instacart
• Anything2Vec, or How Word2Vec Conquered NLP – Yves Peirsman
• Q/A System — Deep learning(2/2) – Becoming Human – Medium
• A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
• Face recognition with Keras and OpenCV – Above Intelligent (AI)
• A16Z AI Playbook
• Neural Text Embeddings for Information Retrieval (WSDM 2017)
• A list of artificial intelligence tools you can use today — for personal use (1/3)
• Research Blog: The Machine Intelligence Behind Gboard
• paraphrase-id-tensorflow/README.md at master · nelson-liu/paraphrase-id-tensorflow
• NeuroNER/README.md at master · Franck-Dernoncourt/NeuroNER
• Generative Adversarial Networks for Beginners - O'Reilly Media
• Neural Translation of Musical Style
• Deep-Learning-Papers-Reading-Roadmap/README.md at master · songrotek/Deep-Learning-Papers-Reading-Roadmap
• AI Progress Measurement | Electronic Frontier Foundation
• How to Visualize Your Recurrent Neural Network with Attention in Keras
• Deep adversarial learning is finally ready 🚀 and will radically change the game
• Franck-Dernoncourt/NeuroNER: Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
• Serving Tensorflow
• How To Install and Use Docker on Ubuntu 16.04 | DigitalOcean
• Jupyter + Tensorflow + Nvidia GPU + Docker + Google Compute Engine
• TensorForce: A TensorFlow library for applied reinforcement learning - reinforce.io
• Exploring LSTMs
• Perform sentiment analysis with LSTMs, using TensorFlow - O'Reilly Media
• Robust Adversarial Examples
• Learning to Learn – The Berkeley Artificial Intelligence Research Blog
• My Curated List of AI and Machine Learning Resources from Around the Web
• goodrahstar/headlines: Automatically generate headlines to short articles
• Deep Learning for NLP Best Practices
• facebookresearch/DrQA: Reading Wikipedia to Answer Open-Domain Questions
• Q/A System — Deep learning(2/2) – Becoming Human
• Contextual Chatbots with Tensorflow – Chatbots Magazine
• Text-Clustering-API/CLAAS_public.py at master · vivekkalyanarangan30/Text-Clustering-API
• tensorflow/nmt: TensorFlow Neural Machine Translation Tutorial
• Cutting Edge Deep Learning for Coders—Launching Deep Learning Part 2 · fast.ai
• facebookresearch/end-to-end-negotiator: Deal or No Deal? End-to-End Learning for Negotiation Dialogues
• examples/main.py at master · pytorch/examples
• BotCube/awesome-bots: A curated awesome list of resources from the bots/AI world by BotCube team. Join our newsletter to get five epic actionable bot tricks delivered to your inbox once a week! 🤖 ❤️
• Thushv » Word2Vec (Part 1): NLP With Deep Learning with Tensorflow (Skip-gram)
• Tensorflow-Programs-and-Tutorials/Question Pair Classification with RNNs.ipynb at master · adeshpande3/Tensorflow-Programs-and-Tutorials
• Intro Deep Learning for Chatbots, Part 2 | Open Data Science
• Named Entity Recognition and the Road to Deep Learning
• Building Convolutional Neural Networks with Tensorflow – Ahmet Taspinar
• Visualising Activation Functions in Neural Networks - dashee87.github.io
• Deep RL Bootcamp - Lectures
• Tensorflow Text Classification - Python Deep Learning - Source Dexter
• my-deity/COMPRESSION_CUM_CLASSIFICATION_v_2.ipynb at master · akanimax/my-deity
• “TensorBoard - Visualize your learning.”
• Cyborg Writer
• Data For Everyone Library | CrowdFlower
• loretoparisi/CapsNet: CapsNet (Capsules Net) in Geoffrey E Hinton paper "Dynamic Routing Between Capsules"
• TFX: A TensorFlow-based production scale machine learning platform | the morning paper
• Flair of Machine Learning
• Recurrent-Highway-Hypernetworks-NIPS/README.md at master · jsuarez5341/Recurrent-Highway-Hypernetworks-NIPS · GitHub
• Using Artificial Intelligence to Augment Human Intelligence
• Learning from Imbalanced Classes - Silicon Valley Data Science
• Regular Expressions for Data Scientists
• Google Developers Blog: Introducing TensorFlow Feature Columns
• bharathgs/Awesome-pytorch-list: A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
• Gaussian Processes – EFavDB
• Neural Smithing
• Hooks Data says…
• Introduction to Various Reinforcement Learning Algorithms. Part I (Q-Learning, SARSA, DQN, DDPG)
• Introduction to Python Ensembles
• Data Science Summit 2018
• facebookresearch/Detectron · GitHub
• GitHub - openai/gradient-checkpointing: Make huge neural nets fit in memory
• Deep-Learning/Skip-Grams-Solution.ipynb at master · priya-dwivedi/Deep-Learning · GitHub
• Recurrent Neural Networks for Drawing Classification  |  TensorFlow
• 2017 news - Gwern.net
• How neural networks are trained
• GitHub - huseinzol05/Emotion-Classification-Comparison: Classification comparison between various models and learning on emotion datasets
• mil-tokyo/webdnn: The Fastest DNN Running Framework on Web Browser
• Beautiful.AI - AI Powered Presentations
• Stanford DAWN Deep Learning Benchmark (DAWNBench) ·
• Introduction to Learning to Trade with Reinforcement Learning – WildML
• Play with Kubernetes
• Deep Reinforcement Learning Doesn't Work Yet
• Release 0.5.0 · PAIR-code/deeplearnjs · GitHub
• Teaching RL
• Introducing the Uber AI Residency
• The Matrix Calculus You Need For Deep Learning

Contributing

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