Deep learning using the MNIST dataset involves training neural networks to perform handwritten digit recognition. The MNIST dataset is a classic benchmark in the field of machine learning, particularly for image classification tasks. It consists of 28x28 grayscale images of handwritten digits (0 to 9) and has a training set of 60,000 images and a testing set of 10,000 images. Deep learning using the MNIST dataset is a fundamental exercise for understanding neural network architectures and optimization algorithms. Once mastered, the knowledge gained can be applied to more complex image recognition tasks, such as character recognition in documents, OCR (Optical Character Recognition), and even real-world applications like reading handwritten digits on checks or recognizing license plate numbers.