Image Classification Using Convolutional Neural Networks
Explore the core concepts of convolutional neural networks, including their architecture and layers, to understand how CNNs process images. This lesson teaches you how CNNs extract patterns and features, apply convolution and pooling operations, and classify images accurately. You will also see how CNNs handle noisy and mislabeled data, preparing you to use them effectively in machine learning tasks involving image data.
In this chapter, we will primarily focus on the fundamentals of convolutional neural networks (CNNs) and their architecture. CNNs are a powerful and widely used approach to handling and analyzing images. They allow us to extract meaningful features and patterns. By exploring the capabilities of CNNs, we can effectively address complex image-related tasks. The knowledge gained in this chapter will enable us to apply CNNs to simulate mislabeled data and analyze the effect of mislabeled data on model performance.
What is a convolutional neural network?
A CNN is a type of deep neural network that effectively identifies patterns in image data. It takes the input images and learns filter (kernel) coefficients that slide over the image to extract features and patterns. These filter values are acquired during the training process through a backpropagation technique (adjusting weight and biases) that enables the ...