var disqus_shortname = 'kdnuggets'; Dealing with Imbalanced Data in Machine Learning, Explaining the Explainable AI: A 2-Stage Approach. Deep Reinforcement Learning sounds intractable for a layperson. I encourage you to check out some of these listed resources, the entire threads, follow @DeepMind on Twitter, and keep your eyes open for additional #AtHomeWithAI hashtags in the coming days and weeks (though, to be honest, I'm not sure how long the campaign is expected to continue). ), and then you add the word “deep” and most people simply drop off. The talk followed the Nature paper on teaching neural networks to play Atari games by Google DeepMind and was intended as a crash course on deep reinforcement learning for the uninitiated. View the entire thread of Julian's suggestions here. DeepMind has recently been sharing such resources via their Twitter account @DeepMind with the hashtag #AtHomeWithAI with the goal of helping you accomplish this very task. Reinforcement Learning 7: Planning and Models. Exploring the Significance of Machine Learning for Algorithmic... Mastering Time Series Analysis with Help From the Experts, Stop Running Jupyter Notebooks From Your Command Line. Reinforcement Learning 5: Function Approximation and Deep Reinforcement Learning. Implementing Async SGD in Python. DeepMind has been sharing resources for learning AI at home on their Twitter account. The threads are linked below, and a selection of the suggested resources are highlighted. Top Stories, Oct 19-25: How to Explain Key Machine Learning Al... How Automation Is Improving the Role of Data Scientists, Ain’t No Such a Thing as a Citizen Data Scientist. At the time of this article's publication, 3 Twitter threads of such resource suggestions have been shared by 3 DeepMind researchers and engineers; Feryal Behbahani (@feryalmp), Julian Schrittwieser (@Mononofu), and Kimberly Stachenfeld (@neuro_kim). The talk followed the Nature paper on teaching neural networks to play Atari games by Google DeepMind and was intended as a crash course on deep reinforcement learning for the uninitiated. Using Ordinary Differential Equations To Design State of the Art Residual-Style Layers, Understanding Attention in Neural Networks Mathematically, Adversarial Dreaming with TensorFlow and Keras, Hogwild!? Students will also find Sutton and Barto’s classic book, Reinforcement Learning: an Introduction a helpful companion. Reinforcement Learning 8: Advanced Topics in Deep RL This lecture series, taught at University College London by David Silver - DeepMind Principal Scienctist, UCL professor and the co-creator of AlphaZero - will introduce students to the main methods and techniques used in RL. (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq); })(); By subscribing you accept KDnuggets Privacy Policy, Information Theory, Pattern Recognition, and Neural Networks, Practical Deep Learning slides and notebooks, The Appeal of Parallel Distributed Processing, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, Free High-Quality Machine Learning & Data Science Books & Courses: Quarantine Edition, Free Mathematics Courses for Data Science & Machine Learning. View the entire thread of Kimberly's suggestions here. This talk details what I’ve learned from replicating the original NIPS/Nature papers on Deep Reinforcement Learning for playing Atari games and walks through some python implementations of the basic steps for reinforcement learning code. KDnuggets 20:n41, Oct 28: Difference Between Junior and Sen... PerceptiLabs – A GUI and Visual API for TensorFlow. “Reinforcement learning” alone sounds scary (I’m reinforcing WHAT again? View the entire thread of Feryal's suggestions here. Data Science, and Machine Learning. Earlier this month, I gave an introductory talk at Data Philly on deep reinforcement learning. Stuck at home? Earlier this month, I gave an introductory talk at Data Philly on deep reinforcement learning. (function() { var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true; dsq.src = 'https://kdnuggets.disqus.com/embed.js'; Random actions? Be sure to stay on the lookout for more #AtHomeWithAI resources from @DeepMind. The talk uses Keras (TensorFlow backend) and tries (albeit, unsuccessfully) to hide advanced mathematical formulations of the problem. Get the slides … Get the slides below! Reinforcement Learning 4: Model-Free Prediction and Control. Looking to take advantage of this reality and expand your knowledge of AI while in quarantine or lockdown? Check out a few of these suggestions here, and keep your eye on the #AtHomeWithAI hashtag for more. The unspoken difference between junior and senior data scientists, Behavior Analysis with Machine Learning and R: The free eBook, Get KDnuggets, a leading newsletter on AI, Brains, Minds & Machines Summer Course This MIT course explores intelligence using an approach integrating cognitive science, neuroscience, computer science and AI. By the end of the talk, we will all have taken a modest step forward in learning deep reinforcement learning. Reinforcement Learning 6: Policy Gradients and Actor Critics. The campaign was introduced on Twitter last week: For students and others interested in expanding their knowledge of AI during this period, we thought it might be helpful to ask our researchers what they consider to be the most impactful and insightful resources available to use #AtHomeWithAI. Getting A Data Science Job is Harder Than Ever – How to ... How to become a Data Scientist: a step-by-step guide. Bah!