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Zbigniew Galar
1,885 followers -
Data analyst and risk management specialist - publisher of toster.pandory.pl
Data analyst and risk management specialist - publisher of toster.pandory.pl

1,885 followers
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How do you get started with deep learning? Siraj Raval says: use Keras. Pytorch is also good but he thinks the best is Keras.
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Natilus hopes to sell its drones to delivery and logistics companies such as Atlas Air (an Amazon partner), UPS, and DHL in a bid to disrupt the US $15.5 trillion global freight market.
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Machine Learning is Fun!

The world’s easiest introduction to Machine Learning. Update: This article is part of a series. Check out the full series: Part 1, Part 2, Part 3, Part 4, Part 5, Part 6, Part 7 and Part 8! You can also read this article in 日本語, Português, Português (alternate), Türkçe, Français, 한국어 , العَرَبِيَّة‎‎, Español (México), Español (España), Polski, Italiano, 普通话, Русский, 한국어 , Tiếng Việt or فارسی. Bigger update: The content of this article is now available as a full-length video course that walks you through every step of the code. You can take the course for free (and access everything else on Lynda.com free for 30 days) if you sign up with this link. Have you heard people talking about machine learning but only have a fuzzy idea of what that means? Are you tired of nodding your way through conversations with co-workers? Let’s change that!
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AI and Deep Learning in 2017 – A Year in Review

The year is coming to an end. I did not write nearly as much as I had planned to. But I’m hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Bayesian Methods coming to WildML! And what better way to start than with a summary of all the amazing things that happened in 2017? Looking back through my Twitter history and the WildML newsletter, the following topics repeatedly came up. I’ll inevitably miss some important milestones, so please let me know about it in the comments!

Reinforcement Learning beats humans at their own games: The biggest success story of the year was probably AlphaGo (Nature paper), a Reinforcement Learning agent that beat the world’s best Go players. Due to its extremely large search space, Go was thought to be out of reach of Machine Learning techniques for a couple more years. What a nice surprise! The first version of AlphaGo was bootstrapped using training data from human experts and further improved through self-play and an adaptation of Monte-Carlo Tree Search. Soon after, AlphaGo Zero (Nature Paper) took it a step further and learned to play Go from scratch, without human training data whatsoever, using a technique a technique previously published in the Thinking Fast and Slow with Deep Learning and Tree Search paper. It also handily beat the first version of AlphaGo. Towards the end of the year, we saw yet another generalization of the AlphaGo Zero algorithm, called AlphaZero, which not only mastered Go, but also Chess and Shogi, using the exact same techniques. Interestingly, these programs made moves that surprised even the most experienced Go players, motivating players to learn from AlphaGo and adjusting their own play style accordingly. To make this easier, DeepMind also released an AlphaGo Teach tool.
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Here's one way that technology can help us deal with a major global issue - drones can help us address the gap between the number of tress we chop down each year (15 billion) and the number of trees planted each year (9 billion)
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All living beings consume energy required for growing, living and repairing the “wear and tear” that occurs along the way. Scale gives us superpowers, but also makes us frail. Sebastian Buckup asks: are we focusing enough energy on maintenance?
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