Convolutional Neural Networks

Machine learning and deep neural networks are of increasing interest to engineers. The current research trend is to use convolutional neural networks (CNNs) to produce state-of-the-art results in various application areas. CNN’s have been around for a while, but they’ve only recently become popular.CNN’s are on the cutting edge of machine learning because they can be trained on more than one task at a time, provide state-of-the-art performance across many domains, and be more easily applied to new tasks than other types of neural networks.

Convolutional Neural Networks (CNNs) is a type of neural network that became popular in the 2010s. CNN’s are used for image recognition tasks, where they outperform other deep learning algorithms. CNN’s are composed of multiple layers of neurons, with each layer performing a convolution operation on the input. Convolutional Neural Networks (CNNs) are neural networks designed to learn and classify visual images efficiently.  A CNN is a neural network with many layers, some of which are convolutions and others fully connected. They work by breaking down the images into perceptive features and then classify them based on these features. CNN can also be used for image recognition or speech recognition tasks. In this course, we will learn how CNNs work and some of the applications they have been used in.

In this course, we will talk about digital images, the convolution process, and pooling features such as max and average pooling. We will also uncover kernels and various filters along with feature maps in the Convolution process of CNN. There is a need of preparing the deep neural network with many layers as they are used to design the learning algorithms. So, We will discuss in this course Batch normalization, which is part of Deep Learning.

Check out our Top Courses in Artificial Intelligence Today.