- DeepMind’s MuZero is One of the Most Important Deep Learning Systems Ever Created - Jan 4, 2021.
MuZero takes a unique approach to solve the problem of planning in deep learning models.
- Facebook Open Sources ReBeL, a New Reinforcement Learning Agent - Dec 14, 2020.
The new model tries to recreate the reinforcement learning and search methods used by AlphaZero in imperfect information scenarios.
- How to Acquire the Most Wanted Data Science Skills - Nov 13, 2020.
We recently surveyed KDnuggets readers to determine the "most wanted" data science skills. Since they seem to be those most in demand from practitioners, here is a collection of resources for getting started with this learning.
- How to Make Sense of the Reinforcement Learning Agents? - Oct 30, 2020.
In this blog post, you’ll learn what to keep track of to inspect/debug your agent learning trajectory. I’ll assume you are already familiar with the Reinforcement Learning (RL) agent-environment setting and you’ve heard about at least some of the most common RL algorithms and environments.
- Deep Learning’s Most Important Ideas - Sep 14, 2020.
In the field of deep learning, there continues to be a deluge of research and new papers published daily. Many well-adopted ideas that have stood the test of time provide the foundation for much of this new work. To better understand modern deep learning, these techniques cover the basic necessary knowledge, especially as a starting point if you are new to the field.
- Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills - Sep 8, 2020.
We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn.
- Will Reinforcement Learning Pave the Way for Accessible True Artificial Intelligence? - Aug 11, 2020.
Python Machine Learning, Third Edition covers the essential concepts of reinforcement learning, starting from its foundations, and how RL can support decision making in complex environments. Read more on the topic from the book's author Sebastian Raschka.
- The Bitter Lesson of Machine Learning - Jul 15, 2020.
Since that renowned conference at Dartmouth College in 1956, AI research has experienced many crests and troughs of progress through the years. From the many lessons learned during this time, some have needed to be re-learned -- repeatedly -- and the most important of which has also been the most difficult to accept by many researchers.
- Deepmind’s Gaming Streak: The Rise of AI Dominance - May 27, 2020.
There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.
- DeepMind’s Suggestions for Learning #AtHomeWithAI - May 13, 2020.
DeepMind has been sharing resources for learning AI at home on their Twitter account. Check out a few of these suggestions here, and keep your eye on the #AtHomeWithAI hashtag for more.
- KDnuggets™ News 20:n19, May 13: Start Your Machine Learning Career in Quarantine; Will Machine Learning Engineers Exist in 10 Years? - May 13, 2020.
Also: The Elements of Statistical Learning: The Free eBook; Explaining "Blackbox" Machine Learning Models: Practical Application of SHAP; What You Need to Know About Deep Reinforcement Learning; 5 Concepts You Should Know About Gradient Descent and Cost Function; Hyperparameter Optimization for Machine Learning Models
- What You Need to Know About Deep Reinforcement Learning - May 12, 2020.
How does deep learning solve the challenges of scale and complexity in reinforcement learning? Learn how combining these approaches will make more progress toward the notion of Artificial General Intelligence.
- DeepMind Unveils Agent57, the First AI Agents that Outperforms Human Benchmarks in 57 Atari Games - Apr 13, 2020.
The new reinforcement learning agent innovates over previous architectures achieving one of the most important milestones in the AI space.
- 2 Things You Need to Know about Reinforcement Learning – Computational Efficiency and Sample Efficiency - Apr 7, 2020.
Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.
- Uber Open Sourced Fiber, a Framework to Streamline Distributed Computing for Reinforcement Learning Models - Apr 6, 2020.
The new framework simplifies distributed and scalable training for reinforcement learning agents.
- 20 AI, Data Science, Machine Learning Terms You Need to Know in 2020 (Part 2) - Mar 2, 2020.
We explain important AI, ML, Data Science terms you should know in 2020, including Double Descent, Ethics in AI, Explainability (Explainable AI), Full Stack Data Science, Geospatial, GPT-2, NLG (Natural Language Generation), PyTorch, Reinforcement Learning, and Transformer Architecture.
- Microsoft Open Sources Jericho to Train Reinforcement Learning Using Linguistic Games - Feb 3, 2020.
The new framework provides an OpenAI-like environment for language-based games.
- A bird’s-eye view of modern AI from NeurIPS 2019 - Jan 28, 2020.
With the explosion of the field of AI/ML impacting so many applications and industries, there is great value coming out of recent progress. This review highlights many research areas covered at the NeurIPS 2019 conference recently held in Vancouver, Canada, and features many important areas of progress we expect to see in the coming year.
- We Created a Lazy AI - Jan 20, 2020.
This article is an overview of how to design and implement reinforcement learning for the real world.
- Xavier Amatriain’s Machine Learning and Artificial Intelligence 2019 Year-end Roundup - Dec 16, 2019.
It is an annual tradition for Xavier Amatriain to write a year-end retrospective of advances in AI/ML, and this year is no different. Gain an understanding of the important developments of the past year, as well as insights into what expect in 2020.
- What just happened in the world of AI? - Dec 12, 2019.
The speed at which AI made advancements and news during 2019 makes it imperative now to step back and place these events into order and perspective. It's important to separate the interest that any one advancement initially attracts, from its actual gravity and its consequential influence on the field. This review unfolds the parallel threads of these AI stories over this year and isolates their significance.
- DeepMind Unveils MuZero, a New Agent that Mastered Chess, Shogi, Atari and Go Without Knowing the Rules - Dec 9, 2019.
The new model showed great improvements over the previous AlphaZero agent.
- The Reinforcement-Learning Methods that Allow AlphaStar to Outcompete Almost All Human Players at StarCraft II - Nov 18, 2019.
The new AlphaStar achieved Grandmaster level at StarCraft II overcoming some of the limitations of the previous version. How did it do it?
- KDnuggets™ News 19:n43, Nov 13: Dynamic Reports in Python and R; Creating NLP Vocabularies; What is Data Science? - Nov 13, 2019.
On KDnuggets this week: Orchestrating Dynamic Reports in Python and R with Rmd Files; How to Create a Vocabulary for NLP Tasks in Python; What is Data Science?; The Complete Data Science LinkedIn Profile Guide; Set Operations Applied to Pandas DataFrames; and much, much more.
- Facebook Adds This New Framework to It’s Reinforcement Learning Arsenal - Nov 11, 2019.
ReAgent is a new framework that streamlines the implementation of reasoning systems.
- Three Things to Know About Reinforcement Learning - Oct 14, 2019.
As an engineer, scientist, or researcher, you may want to take advantage of this new and growing technology, but where do you start? The best place to begin is to understand what the concept is, how to implement it, and whether it’s the right approach for a given problem.
- OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned - Oct 7, 2019.
OpenAI trained agents in a simple game of hide-and-seek and learned many other different skills in the process.
- DeepMind Has Quietly Open Sourced Three New Impressive Reinforcement Learning Frameworks - Sep 30, 2019.
Three new releases that will help researchers streamline the implementation of reinforcement learning programs.
- Collaborative Evolutionary Reinforcement Learning - Jul 8, 2019.
Intel Researchers created a new approach to RL via Collaborative Evolutionary Reinforcement Learning (CERL) that combines policy gradient and evolution methods to optimize, exploit, and explore challenges.
- How Google uses Reinforcement Learning to Train AI Agents in the Most Popular Sport in the World - Jun 21, 2019.
Researchers from the Google Brain team open sourced Google Research Football, a new environment that leverages reinforcement learning to teach AI agents how to master the most popular sport in the world.
- The Emergence of Cooperative and Competitive AI Agents - Jun 19, 2019.
Without specific training in collaboration or competition, a recent AI model from DeepMind uses reinforcement learning to evolve these behaviors in game-playing agents. Learn how this emergent collective intelligence outperforms their human counterparts in 3D multiplayer games.
- ICLR 2019 highlights: Ian Goodfellow and GANs, Adversarial Examples, Reinforcement Learning, Fairness, Safety, Social Good, and all that jazz - May 27, 2019.
We provide an overview of the main themes and topics discussed at this years International Conference on Learning Representations (ICLR).
- Top KDnuggets tweets, May 01-07: The 3 Biggest Mistakes in Learning Data Science; ReinforcementLearning vs. Differentiable Programming; XGBoost Reign - May 8, 2019.
Also XGBoost Algorithm: Long May She Reign; CycleGANs to Create Computer-Generated #Art - #GANs #DeepLearning; Another 10 Free Must-See Courses for Machine Learning and Data Science.
- Another 10 Free Must-See Courses for Machine Learning and Data Science - Apr 5, 2019.
Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.
- My favorite mind-blowing Machine Learning/AI breakthroughs - Mar 14, 2019.
We present some of our favorite breakthroughs in Machine Learning and AI in recent times, complete with papers, video links and brief summaries for each.
- LiveVideo Courses on AI, Big Data, Machine Learning – only $25 through March 31 - Mar 11, 2019.
All Manning live video courses, includes courses on AI, Big Data, Deep Learning, Machine Learning, Reinforcement Learning, and more - are on sale until March 31 - only twenty five dollars.
- 3 Reasons Why AutoML Won’t Replace Data Scientists Yet - Mar 6, 2019.
We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.
- Top 10 Technology Trends of 2019 - Feb 7, 2019.
This article outlines 10 top trending technologies for 2019, a list which covers diverse topics such as security, IoT, reinforcement learning, energy sustainability, smart cities, and much more.
- Top KDnuggets tweets, Jan 30 – Feb 05: state-of-the-art in #AI, #MachineLearning - Feb 6, 2019.
Also Brilliant tour-de-force! Reinforcement Learning to solve Rubiks Cube; Dask, Pandas, and GPUs: first steps; Neural network AI is simple. So Stop pretending you are a genius.
- The 6 Most Useful Machine Learning Projects of 2018 - Jan 15, 2019.
Let’s take a look at the top 6 most practically useful ML projects over the past year. These projects have published code and datasets that allow individual developers and smaller teams to learn and immediately create value.
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
- A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more - Dec 7, 2018.
A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.
- 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- ODSC India Highlights: Deep Learning Revolution in Speech, AI Engineer vs Data Scientist, and Reinforcement Learning for Enterprise - Sep 26, 2018.
Key takeaways and highlights from ODSC India 2018 conference about the latest trends, breakthroughs and revolutions in the field of Data Science and Artificial Intelligence
- Reinforcement Learning: The Business Use Case, Part 2 - Aug 16, 2018.
In this post, I will explore the implementation of reinforcement learning in trading. The Financial industry has been exploring the applications of Artificial Intelligence and Machine Learning for their use-cases, but the monetary risk has prompted reluctance.
- Reinforcement Learning: The Business Use Case, Part 1 - Aug 9, 2018.
At base, RL is a complex algorithm for mapping observed entities and measures into some set of actions, while optimizing for a long-term or short-term reward.
- SuperDataScience Podcast: Insights from the Founder of KDnuggets - Jul 21, 2018.
I talk to Kirill Eremenko about my journey to data science, how KDnuggets started, why you should start honing your machine learning engineering skills at this very moment, what's the future of data science, and more.
- Explaining Reinforcement Learning: Active vs Passive - Jun 26, 2018.
We examine the required elements to solve an RL problem, compare passive and active reinforcement learning, and review common active and passive RL techniques.
- 5 Things You Need to Know about Reinforcement Learning - Mar 28, 2018.
With the popularity of Reinforcement Learning continuing to grow, we take a look at five things you need to know about RL.
- Top KDnuggets tweets, Feb 21-27: Top 20 Python #AI and #MachineLearning Open Source Projects; Intro to Reinforcement Learning Algorithms - Feb 28, 2018.
Also: #NeuralNetwork #AI is simple. So... Stop pretending; 5 Free Resources for Getting Started with #DeepLearning for Natural Language Pro; Want a Job in #Data? Learn This
- Resurgence of AI During 1983-2010 - Feb 16, 2018.
We discuss supervised learning, unsupervised learning and reinforcement learning, neural networks, and 6 reasons that helped AI Research and Development to move ahead.
- NIPS 2017 Key Points & Summary Notes - Dec 18, 2017.
Third year Ph.D student David Abel, of Brown University, was in attendance at NIP 2017, and he labouriously compiled and formatted a fantastic 43-page set of notes for the rest of us. Get them here.
- When reinforcement learning should not be used? - Dec 6, 2017.
While reinforcement learning has achieved many successes, there are situations when it use is problematic. We describe the issues and how to work around them.
- KDnuggets™ News 17:n46, Dec 6: Why You Should Forget for-loop for Data Science Code; Reinforcement Learning: Exclusive Interview with Rich Sutton; Big Data Key Trends - Dec 6, 2017.
Also Big Data: Main Developments in 2017 and Key Trends in 2018; Exclusive: My interview with Rich Sutton, the Father of Reinforcement Learning; Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras.
- Exclusive: Interview with Rich Sutton, the Father of Reinforcement Learning - Dec 5, 2017.
My exclusive interview with Rich Sutton, the Father of Reinforcement Learning, on RL, Machine Learning, Neuroscience, 2nd edition of his book, Deep Learning, Prediction Learning, AlphaGo, Artificial General Intelligence, and more.
- Top KDnuggets tweets, Nov 22-28: Reinforcement Learning: An Introduction by Sutton and Barto – Complete Second Draft - Nov 29, 2017.
Also #DeepLearning Specialization by Andrew Ng - 21 Lessons Learned; How (and Why) to Create a Good Validation Set; Predicting Cryptocurrency Prices With #DeepLearning
- Machine Learning Algorithms: Which One to Choose for Your Problem - Nov 14, 2017.
This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.
- 3 different types of machine learning - Nov 1, 2017.
In this extract from “Python Machine Learning” a top data scientist Sebastian Raschka explains 3 main types of machine learning: Supervised, Unsupervised and Reinforcement Learning. Use code PML250KDN to save 50% off the book cost.
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- AlphaGo Zero: The Most Significant Research Advance in AI - Oct 27, 2017.
The previous version of AlphaGo beat the human world champion in 2016. The new AlphaGo Zero beat the previous version by 100 games to 0, and learned Go completely on its own. We examine what this means for AI.
- AI Conference in San Francisco, Sep 2017 – highlights and key ideas - Sep 28, 2017.
Highlights from recent AI Conference include the inevitable merger of IQ and EQ in computing, Deep learning to fight cancer, AI as the new electricity and advice from Andrew Ng, Deep reinforcement learning advances and frontiers, and Tim O’Reilly analysis of concerns that AI is the single biggest threat to the survival of humanity.
- KDnuggets™ News 17:n37, Sep 27: Essential Data Science & Machine Learning Cheat Sheets; 5 Machine Learning Projects to Check Out Now! - Sep 27, 2017.
30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets; 5 Machine Learning Projects You Can No Longer Overlook - Episode VI; Putting Machine Learning in Production; 5 Ways to Get Started with Reinforcement Learning; Ensemble Learning to Improve Machine Learning Results
- 5 Ways to Get Started with Reinforcement Learning - Sep 20, 2017.
We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.
- Top /r/MachineLearning Posts, August: Andrew Ng is back at it; Reinforcement Learning makes a splash; Fixing your ANN - Sep 8, 2017.
Andrew Ng announces new Deep Learning specialization on Coursera; DeepMind and Blizzard open StarCraft II as an AI research environment; OpenAI bot beat best Dota 2 players in 1v1 at The International 2017; My Neural Network isn't working! What should I do?; Deep Learning Neural Networks Play Path of Exile
- Transforming from Autonomous to Smart: Reinforcement Learning Basics - Aug 11, 2017.
This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
- Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners - Jun 26, 2017.
Here are deep learning demos and examples you can just download and run. No Math. No Theory. No Books.
- Which Machine Learning Algorithm Should I Use? - Jun 1, 2017.
A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.
- 5 Machine Learning Projects You Can No Longer Overlook, May - May 10, 2017.
In this month's installment of Machine Learning Projects You Can No Longer Overlook, we find some data preparation and exploration tools, a (the?) reinforcement learning "framework," a new automated machine learning library, and yet another distributed deep learning library.
- The Next Challenges for Reinforcement Learning - Mar 28, 2017.
Despite the recent success of RL, there is still a lot of work to be done before it will become a mainstream technique. In this blog-post, we look at some of the remaining challenges that are currently being studied.
- Greed, Fear, Game Theory and Deep Learning - Mar 3, 2017.
The most advanced kind of Deep Learning system will involve multiple neural networks that either cooperate or compete to solve problems. The core problem of a multi-agent approach is how to control its behavior.
- Top KDnuggets tweets, Feb 22-28: 50 Companies Leading the #AI Revolution; #AI Nanodegree Program Syllabus - Mar 1, 2017.
50 Companies Leading the #AI Revolution; #AI Nanodegree Program Syllabus: Term 1, In Depth; What is a Support Vector Machine, and Why Would I Use it?; 6 Easy Steps to Learn Naive #Bayes Algorithm (with code in #Python).
- Top arXiv Papers, January: ConvNets Advances, Wide Instead of Deep, Adversarial Networks Win, Learning to Reinforcement Learn - Feb 3, 2017.
Check out the top arXiv Papers from January, covering convolutional neural network advances, why wide may trump deep, generative adversarial networks, learning to reinforcement learn, and more.
- 5 Free Courses for Getting Started in Artificial Intelligence - Feb 1, 2017.
A carefully-curated list of 5 free collections of university course material to help you better understand the various aspects of what artificial intelligence and skills necessary for moving forward in the field.
- 6 areas of AI and Machine Learning to watch closely - Jan 25, 2017.
Artificial Intelligence is a generic term and many fields of science overlaps when comes to make an AI application. Here is an explanation of AI and its 6 major areas to be focused, going forward.
- Eat Melon: A Deep Q Reinforcement Learning Demo in your browser - Jan 20, 2017.
Check "Eat Melon demo", a fun way to gain familiarity with the Deep Q Learning algorithm, which you can do in your browser.
- Deep Learning Research Review: Reinforcement Learning - Nov 25, 2016.
This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.
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- 5 EBooks to Read Before Getting into A Machine Learning Career - Oct 21, 2016.
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
- The Deception of Supervised Learning - Sep 13, 2016.
Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception.
- Top KDnuggets tweets, Aug 03-09: Understanding the Bias-Variance Tradeoff: An Overview - Aug 10, 2016.
Understanding the Bias-Variance Tradeoff: An Overview; Cartoon: Facebook #DataScience experiments and Cats; Bayesian #Machine Learning, Explained; Deep Reinforcement Learning for Keras.
- KDnuggets™ News 16:n29, Aug 10: Data Science for Beginners: Fantastic series; Automating Data Science Contest Winners - Aug 10, 2016.
Data Science for Beginners: Fantastic Introductory Video; Contest 2nd Place: Automating Data Science; Contest Winner: Winning the AutoML Challenge with Auto-sklearn; Reinforcement Learning and the Internet of Things.
- Reinforcement Learning and the Internet of Things - Aug 5, 2016.
Gain an understanding of how reinforcement learning can be employed in the Internet of Things world.
- The Hard Problems AI Can’t (Yet) Touch - Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity,
advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
- An Introduction to Semi-supervised Reinforcement Learning - May 17, 2016.
A great overview of semi-supervised reinforcement learning, including general discussion and implementation information.
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- Are Deep Neural Networks Creative? - May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
- Top KDnuggets tweets, Feb 15-21: Is Big Data Still a Thing? 10 types of #regression. Which one to use? - Feb 24, 2016.
10 types of #regression. Which one to use? Is Big Data Still a Thing? 2016 #BigData Landscape; Demystifying #DeepReinforcement Learning; #TextMining #SouthPark.
- New Machine Learning and Data Science Books – Save 20% - Jun 15, 2015.
New books include Statistical Learning with Sparsity: The Lasso and Generalizations, Statistical Reinforcement Learning: Modern Machine Learning Approaches, and Healthcare Data Analytics. Use Promotion Code GZP42 to save 20% off.