# AI News, Artificial Neural Networks/Recurrent Networks

- On 4. oktober 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.

context layer feeds the hidden layer at iteration N with a value computed from the output of the hidden layer at iteration N-1, providing a short memory effect.

- On 1. oktober 2020

**Deep Learning - Choosing Network Size**

How many nodes and layers do we need? We combine elements of scikit learn and Keras Neural Nets in this lesson.

**Layers in a Neural Network explained**

In this video, we explain the concept of layers in a neural network and show how to create and specify layers in code with Keras. Check out the corresponding ...

**Neural Networks 8: hidden units = features**

**Deep Learning with Tensorflow - The Recurrent Neural Network Model**

Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..

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

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

**Neural Networks 6: solving XOR with a hidden layer**

**Deep Learning with Tensorflow - Activation Functions**

Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..

**Deep Learning with Tensorflow - Recursive Neural Tensor Networks**

Enroll in the course for free at: Deep Learning with TensorFlow Introduction The majority of data ..

**Bias in an Artificial Neural Network explained | How bias impacts training**

When reading up on artificial neural networks, you may have come across the term “bias.” It's sometimes just referred to as bias. Other times you may see it ...

**Overview of a neural network with a hidden layer, 9/2/2015**