We will walk you through the process of developing and evaluating a deep learning (DL) model for multi-class classification problems using DLP with Keras as the backend. If you are looking to develop a DL model for such problems with Caffe as the backend, check out previous tutorials.
In this tutorial, we will introduce you to object detection in DLP using a popular object detection algorithm known as FasterRCNN. We will use person/basketball detection as an example to illustrate how to create a detection model using FasterRCNN in DLP.
In this tutorial we will see how DLP may be used to train a semantic segmentation network for medical imaging. By the end of this tutorial, you will be able to take a single MRI image and produce a labelled output by outlining the location of the left ventricle.
In this hands-on tutorial, we are going to guide you on how to train a convolutional neural network (CNN) classifier on fashion-mnist, using DLP, and without writing a single line of code.
