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 dennybritz.com
Recurrent Neural Networks Tutorial, Part 3 - Backpropagation Through Time and Vanishing Gradients · Denny's Blog
| | www.v7labs.com
Activation Functions in Neural Networks [12 Types & Use Cases]
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| | A neural network activation function is a function that is applied to the output of a neuron. Learn about different types of activation functions and how they work.
| | erikbern.com
Recurrent Neural Networks for Collaborative Filtering · Erik Bernhardsson
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| | I've been spending quite some time lately playing around with RNN's for collaborative filtering. RNN's are models that predict a sequence of something. The beauty is that this something can be anything really - as long as you can design an output gate with a proper loss function, you can model essentially anything.
| | blog.owulveryck.info
About Recurrent Neural Network, Shakespeare and GO - Unladen swallow - Olivier Wulveryck
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| | You may know how enthusiast I am about machine learning. A while ago I discovered recurrent neural networks. I have read that this 'tool' allow to predict the future! Is this a kind of magic? I have read a lot of stuffs about the 'unreasonable effectiveness' of this mechanism. The litteracy that gives deep explanation exists and is excellent. There is also plehtora of examples, but most of them are using python and a calcul framework. To fully undestand how things work (as I am not a data-scientist), I needed to write my own tool 'from scratch'. This is what this post is about: a more-or-less 'from scratch' implementation of a RNN in go that can be used to applied to a lot of examples
| | research.google
Transformer: A Novel Neural Network Architecture for Language Understanding
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| Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are n...