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Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired by Google. They used neural networks to create algorithms that are able to play Atari games, learn them like a human would, eventually achieving superhuman performance.

Deep learning means that we use artificial neural network with multiple layers, making it even more powerful for more difficult tasks. These machine learning techniques proved to be useful for many tasks beyond image recognition: they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others.

In this episode, an intuitive explanation is given to show the inner workings of deep learning algorithms.

________________________

Original blog post by Christopher Olah (source of many images):
http://colah.github.io/posts/2....014-03-NN-Manifolds-

You can train your own deep neural networks on Andrej Karpathy's website:
http://cs.stanford.edu/people/....karpathy/convnetjs/d

Images used in this video:
Bunny by Tomi Tapio K (CC BY 2.0) - https://flic.kr/p/8EbcEk
Train by B4bees (CC BY 2.0) - https://flic.kr/p/6RzHe4
Train with bunny by Alyssa L. Miller (CC BY 2.0) - https://flic.kr/p/5WPeRN
The knot theory blackboard image was created by Clayton Shonkwiler (CC BY 2.0) https://flic.kr/p/64FYv
The tangled knot image was created by Mikael Hvidtfeldt Christensen (CC BY 2.0) https://flic.kr/p/beYG9D

The thumbnail image is a work of Duncan Hull (CC BY 2.0) - https://flic.kr/p/98qtJB

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Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu

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