Creating a custom image classification model is challenging, but the existence of neural network libraries like Keras has made it doable. Here's how, with many code samples and a full project download ...

For the MNIST data set, the input images are handwritten digits in the range 0 to 9 [10]. The training and test data sets contain 60,000 and 10,000 labeled images, respectively.

An example of an image classification problem is to identify a photograph of an animal as a "dog" or "cat" or "monkey." The two most common approaches for image classification are to use a standard ...