Neural Network (Initial Idea 2)

Neural Networks

 Neural Networks, colloquially known as artificial neural networks (ANNs) and also simulated neural networks (SNNs), are a sub-fiend of machine learning which in it self is a sub-field of Artificial Intelligence, they are the brain of AI and are components of deep learning. The name and structure of NNs are similar to the human brain and were inspired by it, they imitate the biological neurons by signaling to each other (IBM, 2020). ANNs consist of an input layer of neurons, one or two hidden layers of neurons and a final layer of output neurons (Wang, 2003).

 The diagram illustrated exemplifies a simple architecture of a neural network, the lines which connect the neurons are called weights and are associated with a number which represent the strength of connection between neurons.

 There are many different types of neural networks and each are used for specific motives. Below are some of the most common ones used in the field:

The perceptron “the oldest neural network, created by Frank Rosenblatt in 1958” (IBM, 2020).

Recurrent neural networks (RNNs) “are a class of neural networks that allow previous outputs to be used as inputs while having hidden states” (Stanford university,2021).

Convolutional neural networks (CNNs) “These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image” (IBM, 2020).

Feedforward neural networks and these are the opposite of RNNs, they do not form a cycle in the weights (IBM, 2020).

 

 

 

 

 

References:

Wang, SC. (2003) Artificial neural network. In: Interdisciplinary Computing in Java Programming. Boston MA: Springer, pp 1.

IBM, 2021. What are Neural Networks?. [online] Ibm.com. Available at: <https://www.ibm.com/cloud/learn/neural-networks> [Accessed 18 April 2021].

Stanford University, 2021. CS 230 - Recurrent Neural Networks Cheatsheet. [online] Stanford.edu. Available at: <https://stanford.edu/~shervine/teaching/cs-230/cheatsheet-recurrent-neural-networks> [Accessed 18 April 2021].

Gad, A., 2021. neural network. [image] Available at: <https://pixabay.com/illustrations/artificial-neural-network-ann-3501528/> [Accessed 20 April 2021].