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Implementing the fish recognition/detection model

To introduce the power of machine learning and deep learning in particular, we are going to implement the fish recognition example. No understanding of the inner details of the code will be required. The point of this section is to give you an overview of a typical machine learning pipeline.

Our knowledge base for this task will be a bunch of images, each one of them is labeled as opah or tuna. For this implementation, we are going to use one of the deep learning architectures that made a breakthrough in the area of imaging and computer vision in general. This architecture is called Convolution Neural Networks (CNNs). It is a family of deep learning architectures that use the convolution operation of image processing to extract features from the images that can explain the object that we want to classify. For now, you can think of it as a magic box that will take our images, learn from it how to distinguish between our two classes (opah and tuna), and then we will test the learning process of this box by feeding it with unlabeled images and see if it's able to tell which type of fish is in the image.

Different types of learning will be addressed in a later section, so you will understand later on why our fish recognition task is under the supervised learning category.

In this example, we will be using Keras. For the moment, you can think of Keras as an API that makes building and using deep learning way easier than usual. So let's get started! From the Keras website we have:

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.