Deep learning: Deep learning is a part of Machine learning in which used to achieve artificial intelligence.

How deep learning works? Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks.

The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150. One of the most popular types of deep neural networks is known as convolutional neural networks (CNN or ConvNet).

A CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images. CNNs eliminate the need for manual feature extraction, so you do not need to identify features used to classify images. The CNN works by extracting features directly from images. The relevant features are not pretrained; they are learned while the network trains on a collection of images. This automated feature extraction makes deep learning models highly accurate for computer vision tasks such as object classification.

CNNs learn to detect different features of an image using tens or hundreds of hidden layers. Every hidden layer increases the complexity of the learned image features. For example, the first hidden layer could learn how to detect edges, and the last learns how to detect more complex shapes specifically catered to The shape of the object we are trying to recognize.

To know more about how deep learning works you can use this link to refer: simplilearn.com/tutorials/deep-learning-tut..

Done by MAREESWARI S

16 JAN 2022