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Image Recognition with Convolutional Neural Networks (CNNs)
Learn how to implement image recognition with CNNs using TensorFlow.js
Image recognition is a crucial task in the field of computer vision, and Convolutional Neural Networks (CNNs) have become the state-of-the-art method for this task. CNNs are a type of neural network that are specifically designed for image recognition tasks, using convolutional layers to extract features from images and fully connected layers to make predictions.
In this article, we’ll explore how to implement image recognition with CNNs, using the popular Python library, TensorFlow. We’ll use the MNIST dataset, which is a collection of handwritten digits, to train our model and make predictions.
The Basics of Convolutional Neural Networks
In a traditional neural network, the input is a vector of features, and the output is a prediction. However, in the case of image recognition, the input is an image, which can be represented as a 2D array of pixel values. To process this input, we need to extract features from the image that are meaningful for making predictions.
The first layer of a CNN is the convolutional layer, which performs a convolution operation on the input image to extract features. The convolution operation…
