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Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms

Stochastic Approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. The most famous examples today are TD- and Q-learning algorithms. This three hour tutorial lecture series, courtesy of the Simon Institute for the Theory of Computing at UC Berkeley, consists of two video segments.

Importance of Using Data and Analysis in Higher Education

Using the right data, in the right way, can help educational institutions and leaders keep up with ongoing challenges. Data analysis consolidates information to provide the big picture of trends and patterns for higher education leadership teams that can be used to evaluate and streamline processes, create efficiencies, and improve the overall student experience. The infographic below from our friends over at Maryville University highlights the importance of data and analysis in higher education.

Interview: Vivienne Sze, Associate Professor of Electrical Engineering and Computer Science at MIT

I recently caught up with Vivienne Sze, Associate Professor of Electrical Engineering and Computer Science at MIT, to discuss the launch a new professional education course titled, “Designing Efficient Deep Learning Systems.” The two-day class will run March 28-29, 2018 at the Samsung Campus in Mountain View, CA and will explore all the latest breakthroughs related to efficient algorithms and hardware that optimize power, memory and data processing resources in deep learning systems.

deepsense.ai Popularizes Machine Learning at European Universities as Part of the Intel Nervana AI Academy

deepsense.ai will train hundreds of European students in the upcoming three months as part of the Intel® Nervana™ AI Academy. Students will get the valuable opportunity to gain practical knowledge in two of the most cutting‑edge and fast developing areas of data science, machine learning and deep learning.

Book Review: Python Data Science Handbook

I recently had a need for a Python language resource to supplement a series of courses on Deep Learning I was evaluating that depended on this widely used language. As a long-time data science practitioner, my language of choice has been R, so I relished the opportunity to dig into Python to see first hand how the other side of the data science world did machine learning. The book I settled on was “Python Data Science Handbook: Essential Tools for Working with Data” by Jake VanderPlas.

Field Report: Deep Learning Specialization on Coursera

This “Field Report” is a bit difference from all the other reports I’ve done for insideBIGDATA.com because it is more of a “virtual” report that chronicles my experiences going through the content of an exciting new learning resource designed to get budding AI technologists jump started into the field of Deep Learning. Renowned MOOC platform Coursera just launched a new Deep Learning Specialization series consisting of 5 courses.

NVIDIA to Train 100,000 Developers on Deep Learning in 2017

[GPU Technology Conference Coverage] To meet surging demand for expertise in the field of AI, NVIDIA announced that it plans to train 100,000 developers this year — a tenfold increase over 2016 — through the NVIDIA Deep Learning Institute. Analyst firm IDC estimates that 80 percent of all applications will have an AI component by 2020. The NVIDIA Deep Learning Institute provides developers, data scientists and researchers with practical training on the use of the latest AI tools and technology.

Online Education Gets Up Close and Personal as Big Data Improves Performance

In this special guest feature, Jerry Huang, Chief Operating Officer of iTutorGroup, discusses how the collection and analysis of Big Data will continue to dramatically change the landscape of learning and pave the way for optimized learning experiences, leading to even better student outcomes.

Quantifying Data Science

Our friends over at The Data Incubator just released a new series of data-driven ranking reports that showcase the quantitative methodologies the data science fellowship, hiring and training company uses to actively teach their fellows. The idea was to develop a more data-driven approach to what the company should be teaching in their data science corporate training and their free fellowship for masters and PhDs looking to enter data science careers in industry.

Making the Leap from Data Science Hopeful to Practitioner

It’s a familiar dilemma. You’ve done your research, read some books, taken some online classes – and at long last, you’re finally ready to get real-life work experience as a Data Scientist. In this contributed article, Dan Saber, Data Science Hiring Manager at Coursera, offers three important steps for successfully transitioning into a data science career.