A popular demonstration of the capability of deep learning techniques is object recognition in image data.
The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition.
Here you will discover how to develop a deep learning model to achieve near state of the art performance on the MNIST handwritten digit recognition task in Python using the Keras deep learning library.
Each image is a 28 by 28 pixel square (784 pixels total). A standard split of the dataset is used to evaluate and compare models, where 60,000 images are used to train a model and a separate set of 10,000 images are used to test it.
It is a digit recognition task. As such there are 10 digits (0 to 9) or 10 classes to predict. Results are reported using prediction error, which is nothing more than the inverted classification accuracy.
Next: Object Recognition in CIFAR-10 with Convolutional Neural Networks