# AI News, Artificial Neural Networks/Recurrent Networks

- On Thursday, October 4, 2018
- By Read More

## 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 Tuesday, March 26, 2019

**Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka**

TensorFlow Training - ) This Edureka Recurrent Neural Networks tutorial video (Blog: ..

**Lecture 10 | Recurrent Neural Networks**

In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for language ...

**Neural Network Calculation (Part 1): Feedforward Structure**

From In this series we will see how a neural network actually calculates its values. This first video takes a look at the structure of ..

**MIT 6.S094: Recurrent Neural Networks for Steering Through Time**

This is lecture 4 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Course website: Lecture 4 slides: ..

**3. Hopfield Nets with Hidden Units**

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

**Multilayer Neural Network**

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

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

**Lecture 10 - Neural Networks**

Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech's Machine ...

**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?

**Radial Basis Function Artificial Neural Networks**

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