Deep learning for complete beginners: Recognising handwritten digits by Cambridge Coding Academy

Welcome to the first in a series of blog posts that is designed to get you quickly up to speed with deep learning; from first principles, all the way to discussions of some of the intricate details, with the purposes of achieving respectable performance on two established machine learning benchmarks: MNIST (classification of handwritten digits) and CIFAR-10 (classification of small images across 10 distinct classes--airplane, automobile, bird, cat, deer, dog, frog, horse, ship & truck). The accelerated growth of deep learning has lead to the development of several very convenient frameworks, which allow us to rapidly construct and prototype our models, as well as offering a no-hassle access to established benchmarks such as the aforementioned two. The particular environment we will be using is Keras, which I've found to be the most convenient and intuitive for essential use, but still expressive enough to allow detailed model tinkering when it is necessary. By the end of this part of the tutoral, you should be capable of understanding and producing a simple multilayer perceptron (MLP) deep learning model in Keras, achieving a respectable level of accuracy on MNIST. The next tutorial in the series will explore techniques for handling larger image classification tasks (such as CIFAR-10).