For this tutorial in my Reinforcement Learning series, we are going to be exploring a family of RL algorithms called Q-Learning algorithms. These are a little different than the policy-based algorithms that will be looked at in the the following tutorials (Parts 1–3). Instead of starting with a complex and unwieldy deep neural network, we will begin by implementing a simple lookup-table version of the algorithm, and then show how to implement a neural-network equivalent using Tensorflow. Given that we are going back to basics, it may be best to think of this as Part-0 of the series. It will hopefully give an intuition into what is really happening in Q-Learning that we can then build on going forward when we eventually combine the policy gradient and Q-learning approaches to build state-of-the-art RL agents (If you are more interested in Policy Networks, or already have a grasp on Q-Learning, feel free to start the tutorial series here instead).

There are ten webinars in four days, and SIG is hosting their Spring Summit in Washington D.C. from the 26th through the 29th (be sure to seek out Phil Ideson if you're going). If you like to plan further ahead, I recommend "Trade Wars & Supply Chains: Measuring and Managing the Impact" from Supply & Demand Chain Executive and Resilinc on April 26th at 1pm ET. Click on the title of each recommended webinar below to view the full description and register. BTW: If you haven't already, sign up for our mailing list to be sure you get my weekly recommendations in your Inbox each Monday. There has been so much hype around technologies such as AI, machine learning, and cognitive computing that they've practically achieved "IT SLICES!

Machine learning helps pinpoint errors in large datasets for cleansing before entering the analytics pipeline. This on-demand webinar shows you how to set it up. Big data brings tremendous opportunity to better target customers and improve operations. Yet, data-driven insights are only as good and trusted as the data going into them. Find out how you can build data quality into your structured, semi-structured, or unstructured data on Microsoft Azure Data Lake Store and HDInsight using Talend's native support for Spark machine learning algorithms.

Employees at all stages of their careers are challenged by the technological and socio-economical changes that are limiting the suitability of these employee's current skills and learning. Widening gap between the skills available and skills in demand is certainly alarming and you should not overlook a timely career advice. To brace yourself for a future-ready career you will require advanced technical training or specialized education. Dynamic re-skilling and learning on-the-go are keys to be successful in the competitive job market. Everybody is talking about Artificial Intelligence.

I've found the review on the first three courses by Arvind N very useful in taking the decision to enroll in the first course, so I hope, maybe this can also be useful for someone else. Taking the five courses is very instructive. The content is well structured and good to follow for everyone with at least a bit of an understanding on matrix algebra. Some experience in writing Python code is a requirement. The programming assignments are well designed in general.

In these notes we describe heuristics to predict computational-to-statistical gaps in certain statistical problems. These are regimes in which the underlying statistical problem is information-theoretically possible although no efficient algorithm exists, rendering the problem essentially unsolvable for large instances. The methods we describe here are based on mature, albeit non-rigorous, tools from statistical physics. These notes are based on a lecture series given by the authors at the Courant Institute of Mathematical Sciences in New York City, on May 16th, 2017.

We hope you'll join us on May 9th at 10:00 AM ET in Duke's Trent Semans Center Great Hall for the first Duke Forge Health Data Science Symposium: Health Science and the Four Fs: Foibles, Frontiers, Fit-for-Purpose, and the Future of a Learning Health System. For those who are unable to attend in person, a remote videoconference option will be available. During the 3-hour workshop moderated by Forge Director Robert M. Califf, MD, our panel of experts will explore the intersection of classical statistics, machine learning, and the full spectrum of knowledge and tools needed to engage in actionable health data science. The symposium is free but registration is required. A detailed agenda is available on the Forge website.

Lucas asserted "... technology-enhanced teaching and learning can dramatically improve the quality and success of higher education ..." His Figure 1 and Figure 2, in outlining traditional versus technology-enhanced courses, suggested traditional teaching methods deliver a low-quality result, while professional (Hollywood) production methods deliver a high-quality result, with, again, no evidence provided. The idea of universities as "content producers" giving students "content" consisting of "course materials and exercises" gave me an analogous idea. Families give food and clothing to their children, but families are inefficient and can involve bloated administrations (parents). Just as parents do more than feed (they try to create an environment where their children can develop and thrive), universities likewise try to create a learning environment for students. Indispensable elements include laboratory work, fieldwork, real essays marked by real scholars (not against a list of bullet points), and project work.

Earlier I wrote about How to Reinvent Yout Career With AI Skills.This isn't a time to relax and think what should you learn next? Build skills around what's one of the most significant technologies of the coming decade –and that is Artificial Intelligence. Despite recent growth in interest, AI is a skill possessed by relatively few people. Many of the roles, needed skills and business titles of the future are unknown to us. Talent is no longer same as it used to be five years before.

The ability to manipulate and understand data is increasingly critical to discovery and innovation. As a result, we see the emergence of a new field--data science--that focuses on the processes and systems that enable us to extract knowledge or insight from data in various forms and translate it into action. In practice, data science has evolved as an interdisciplinary field that integrates approaches from such data-analysis fields as statistics, data mining, and predictive analytics and incorporates advances in scalable computing and data management. But as a discipline, data science is only in its infancy. The challenge of developing data science in a way that achieves its full potential raises important questions for the research and education community: How can we evolve the field of data science so it supports the increasing role of data in all spheres? How do we train a workforce of professionals who can use data to its best advantage? What should we teach them? What can government agencies do to help maximize the potential of data science to drive discovery and address current and future needs for a workforce with data science expertise?