Deep learning is an integral part of AI that uses multiple levels of processing to process data similarly to a human brain. Neural networks are an important component of deep learning, allowing AI to recognize words and images like humans can. What are some examples of neural networks?

‍

1. Generative Adversarial Neural Networks

‍

Generative Adversarial Neural Networks contain two networks: Generator networks and Discriminator networks. Generator networks generate fake data, while discriminator networks are tasked with recognizing fake data. When the discriminator gets better at recognizing fake data, the generator eventually starts producing more and more realistic data. This leads to more realistic images, video, and audio.

‍

2. Recurrent Neural Networks

‍

Recurrent Neural Networks are primarily used to detect patterns, because they store previous inputs in their memory. This memory is composed of previous and current inputs. This makes them useful in analyzing data, because they can detect patterns within it.

‍

3. Convolutional Neural Networks

‍

Convolutional Neural Networks are used extensively in image recognition. The first layer of processing, called the convolution layer, applies filters to an image so it can recognize differences. The pooling layer simplifies the parameters so the model doesn’t have to put in as much time to learn them. These layers are applied many times until the model gets better.

‍

‍

‍

Works Cited

https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm

‍

https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939

‍

https://poloclub.github.io/cnn-explainer/

‍

https://www.mathworks.com/discovery/convolutional-neural-network.html

‍