# AI News, Difference between revisions of "Artificial Neural Networks/Recurrent Networks"

- On Thursday, October 4, 2018
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## Difference between revisions of "Artificial Neural Networks/Recurrent Networks"

In a recurrent network, the weight matrix for each layer l contains input weights from all other neurons in the network, not just neurons from the previous layer.

Recurrent networks, in contrast to feed-forward networks, do have feedback elements that enable signals from one layer to be fed back to a previous layer.

A set of additional context units are added to the input layer that receive input from the hidden layer neurons.

- On Thursday, October 17, 2019

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**3. Hopfield Nets with Hidden Units**

Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:

**Multilayer Neural Network**

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We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use ...

**Neural networks [1.4] : Feedforward neural network - multilayer neural network**

**4. Why it is Difficult to Train an RNN**

Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:

**The Future of Deep Learning Research**

Back-propagation is fundamental to deep learning. Hinton (the inventor) recently said we should "throw it all away and start over". What should we do?