AI News, Artificial Neural Networks/Recurrent Networks

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.

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

My web page: