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Much of the recent buzz has been due to advances in [deep learning](https://en.wikipedia.org/wiki/Deep\\_learning) coupled with GPU techniques for employing neural network algorithms.   \n  \n2. The aforementioned [Google research blog post](http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html). As far as I can tell, this was first [leaked on reddit](https://www.reddit.com/r/MachineLearning/comments/3a1ebc/image\\_generated\\_by\\_a\\_convolutional\\_network/) a day or so before the Google blog post went up. This [2014 preprint](http://arxiv.org/pdf/1412.6296v2.pdf) from Microsoft research has some more, similar neat images, as well as insight into a related set of techniques. Honestly, the whole [machine learning subreddit](https://www.reddit.com/r/MachineLearning/) is pretty great.   \n  \n3. A really great [blog post](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) on the unreasonable effectiveness of recurrent neural networks. Includes a Paul Graham text generator, a Wikipedia text generator, a LaTeX text generator, and a Linux code generator. Also includes source code if you want to build your own!   \n  \n4. UC Berkeley's own Vision and Learning Center hosts a really great open source toolkit for working with neural networks called [Caffe](http://caffe.berkeleyvision.org/).   \n  \n5. [A slightly processor intensive site](http://soumith.ch/eyescream/) which performs live image generation via an adversarial neural network. Essentially, a network tries to produce an image that will \"look real\" for another function's definition of \"real\"-ness.","image":{"publicURL":"/static/fe9d4c26eaf7b188cb37d478ad4a49e4/60c863dc36cdd023eb406de09b6d52ed.png"},"authors":[{"id":"6030b07d7782326a48b057cd","name":"Daniel Freeman"}],"designers":[],"categories":[{"id":"6030b07d7782326a48b058c4","title":"Archive"}],"magazine":null},"recent":{"edges":[{"node":{"id":"Article_6a1b7b70a1914505ee622e8a","title":"A quarter-century of BSR","authors":[{"id":"6930d4c5a1914505ee622e05","name":"Eleanor Wang"}],"categories":[],"image":{"publicURL":"/static/e2535987c5657a0ccb90aefc70d390fa/d0381637f7e57d0c526bc22c6b39f421.png"},"published_at":"2026-05-31T19:32:44.843Z"}},{"node":{"id":"Article_6a1c7dfda1914505ee622e93","title":"A taste of Ohlone culture","authors":[{"id":"6a1b3e7ea1914505ee622e74","name":"Jack Schill"}],"categories":[{"id":"61fdc5cd1e0c2a0a6f3372c1","title":"Science and 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