For example, rather than directly taking an input vector $\\v=\begin{bmatrix} x_{1}\\ x_{2}\\ \vdots\\ x_{n} \end{bmatrix}\\$ and activating the neural network to produce certain output $\\y\\$, it should take a series of binary vectors that add up to the main vector. A vanilla neural network takes in a fixed size vector as input which limits its usage in situations that involve a ‘series’ type input with no predetermined size. But despite their recent popularity I’ve only found a limited number of resources that throughly explain how RNNs work, and how to implement them. We propose a new recognition model called Concurrent Neural Networks (CNN), representing a winner-takes-all collection of neural networks.

Recurrent Neural Networks Tutorial, Part 1 – Introduction to RNNs Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. Recurrent Neural Networks (RNNs) add an interesting twist to basic neural networks. I am trying to implement neural networks that can take concurrent input and compute the output.

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