AI Inference Software Fundamentals: Getting Started with Optical Character Recognition

You can find the full source code to today's demo in a Kaggle notebook where it is formatted as a series of very short, numbered blocks. For the sake of brevity, this post will walk through only the most significant snippets of the notebook's code. But, of course, you can study the full notebook at your leisure by the block number and learn how we trained a neural network from scratch to achieve a level of accuracy not possible a decade ago. In blocks 1 to 3, the notebook sets the Python environment for TensorFlow. In blocks 4 to 14, the notebook loads the database MNIST, which is what we will use to create a model that can recognize handwritten digits and train our neural networks. Then the new and exciting part Intel offers today is how these models can be optimized on Intel hardware to run more efficiently and quickly.