The visual cortex of the human brain can effortlessly recognize objects, but can a computer do this as efficiently?
Convolutional Neural Network
Recurrent Neural Network
Generative Adversarial Network
The human like ability of Neural Networks spans from recognizing handwritten digits to 'drawing' (generating) completely new objects, faces, animals etc. There are different types of neural networks, as some of them are mentioned above. They all can accomplish different tasks. For example, CNNs are a broad type of neural networks that are used in various other types such as GANs for example. CNNs learn patterns in visual representations and are able to detect similar patterns later. GANs completely revolutionized the field of Deep Learning (practice of neural networks) and they are able to learn how to generate visual representations that do not exist. For example: they can learn how to generate an image of a cat or a completely new human face. RNNs are used with sequential data, for example if we wanted to predict the next word in a sentence based on the previous words "I am going for a" RNN can be applied. Since a sentence is a sequence of words a RNN could successfully guess the word "walk" in this case.More Info