Image classification using Convolutional Neural Networks (CNNs) with TensorFlow and Keras.
Image classification is a field of study within the broader area of computer vision and deep learning, with the goal of automating the task of categorizing images into different classes. One of the most popular and widely used techniques for image classification is Convolutional Neural Networks (CNNs). In this article, we will explain the basics of CNNs and demonstrate how to build an image classifier using TensorFlow and Keras, two popular open-source deep learning frameworks.
What are Convolutional Neural Networks (CNNs)?
Convolutional Neural Networks are a type of deep learning architecture that is specifically designed for image classification. They are called “convolutional” because they use a convolution operation to scan the input image for relevant features. The basic building blocks of a CNN are convolutional layers, activation functions, pooling layers, and fully connected layers.
The convolutional layer performs a convolution operation on the input image, where each element in the convolutional kernel is multiplied by the corresponding element in the input image, and the results are summed up. The activation function is applied to the output of the convolutional layer to introduce non-linearity into…
