/explore
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clemenswinter.com | ||
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blog.otoro.net
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| | | | | We survey ideas from complex systems such as swarm intelligence, self-organization, and emergent behavior that are gaining traction in ML. (Figure: Emergence... | |
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blog.fastforwardlabs.com
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| | | | | Research in machine learning has seen some of the biggest and brightest minds of our time - and copious amounts of funding - funneled into the pursuit of better, safer, and more generalizable algorithms. As the field grows, there is vigorous debate around the direction that growth should take (for a less biased take, see here). This week, I give some background on the major algorithm types being researched, help frame aspects of the ongoing debate, and ultimately conclude that there is no single direction to build toward - but that through collaboration, we'll see advances on all fronts. | |
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deepmind.google
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| | | | | Games have been used for decades as an important way to test and evaluate the performance of artificial intelligence systems. As capabilities have increased, the research community has sought... | |
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jaketae.github.io
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| | | Recently, a friend recommended me a book, Deep Learning with Python by Francois Chollet. As an eager learner just starting to fiddle with the Keras API, I decided it was a good starting point. I have just finished the first section of Part 2 on Convolutional Neural Networks and image processing. My impression so far is that the book is more focused on code than math. The apparent advantage of this approach is that it shows readers how to build neural networks very transparently. It's also a good introduction to many neural network models, such as CNNs or LSTMs. On the flip side, it might leave some readers wondering why these models work, concretely and mathematically. This point notwithstanding, I've been enjoying the book very much so far, and this post is... | ||
