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The Google Developers Show: Episode TL;DR 138

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The Google Developer Show brings you the latest developer news from across Google. Your host for this week is Developer Advocate Dan Galpin ( Expect a new Dev Show episode every week, and let us know what you think of the latest announcements in the comments below!

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A Day In The Life Of A Computer Software Engineer – Vlog

hey YouTube! I work as a software engineer and here’s my typical day.

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Channel: Life of Luba
Published: 2018-03-06 07:05:37
Duration: 3M7S
Views: 514615
Likes: 6607
Favorites: 0

Quantum computing explained with a deck of cards | Dario Gil, IBM Research

We are moving rapidly toward quantum computing. How does the technology work and what does it mean for our future? Scientist Dario Gil, VP of Science and Solutions at IBM, provides clarity on this complex topic. David Morczinek gives the introduction.

The MIT Venture Capital + Innovation Conference is held annually in February at the MIT Sloan School of Management. Thank you to our lead sponsor IBM Research, as well as Solvay, Wilmer Hale, Finnegan, The MIT Industrial Liaison Program, the MIT Startup Exchange, and Startup Hub Boston. Visit

Dr. Gil is a leading technologist and senior executive at IBM. As Vice President of Science and Solutions of IBM Research, Dr. Gil directs a global organization of some 1,500 researchers across 11 laboratories. He has direct responsibility for IBM’s science agenda, with a broad portfolio of activities spanning the physical sciences, the mathematical sciences, healthcare and the life sciences. Dr. Gil is also responsible for IBM’s cognitive solutions research agenda, which aims to create scientific and technological breakthroughs to differentiate IBM’s solutions businesses and serves as an incubator for future cognitive industry solutions for IBM and its clients.
Prior to his current position, Dr. Gil was the Director of Symbiotic Cognitive Systems, where he led the creation of cognitive environments, highly interactive physical spaces designed to improve the quality of decision-making through always-on ambient intelligence. During his tenure he was responsible for the design and creation of three pioneering laboratories and experiential centers: the Cognitive Environments Laboratory, the IBM Research THINKLab and the IBM Watson Experience Center.

Dr. Gil is a passionate advocate of collaborative research business models and is the creator and Founding Director of two research consortia: the IBM Research Frontiers Institute and the Smarter Energy Research Institute. An expert in the field of nanofabrication, he led the team that built the world’s first microprocessor with immersion lithography in 2004.

Dr. Gil is a frequent speaker at business events, conferences (including TED), universities, research institutions and foundations. His research results have appeared in over 20 international journals and conferences, and he is the author of numerous patents. Dr. Gil is a member of the Future Trends Forum, the Industrial Advisory Group of the Institute of Photonic Sciences and an elected member of the IBM Academy of Technology. He received his Ph.D. in electrical engineering and computer science from the Massachusetts Institute of Technology.

Channel: MIT Venture Capital & Innovation
Published: 2017-06-21 18:07:49
Duration: 16M35S
Views: 611021
Likes: 5007
Favorites: 0

We built the Home of the Future with Grant Imahara

The Verge and Curbed have teamed up to build the home of the future. Join host Grant Imahara as he examines the renewed trend of prefabricated modular home construction.


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Channel: The Verge
Published: 2018-08-03 21:48:50
Duration: 7M23S
Views: 1438517
Likes: 35261
Favorites: 0

What Makes a Good Feature? – Machine Learning Recipes #3

Good features are informative, independent, and simple. In this episode, we’ll introduce these concepts by using a histogram to visualize a feature from a toy dataset. Updates: many thanks for the supportive feedback! I’d love to release these episodes faster, but I’m writing them as we go. That way, I can see what works and (more importantly) where I can improve.

We’ve covered a lot of ground already, so next episode I’ll review and reinforce concepts, introduce clearer syntax, spend more time on testing, and continue building intuition for supervised learning.

I also realize some folks had dependency bugs with Graphviz (my fault!). Moving forward, I won’t use any libraries not already installed by Anaconda or Tensorflow.

Last: my code in this cast is similar to these great examples. You can use them to produce a more polished chart, if you like:

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Channel: Google Developers
Published: 2016-04-27 15:56:13
Duration: 5M41S
Views: 369614
Likes: 4043
Favorites: 0

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