When it comes to solving a problem the deep learning perspective is very different from the standard development approach. Instead of designing an algorithm to achieve a solution you find yourself looking at the data (preferably Big Data), trying to find out a good way to normalize them and to represent them, both as input and as output, in an effective way to the neural network.
Eclipse Deeplearning4j (DL4J) allows you to get into the play quicky, leveraging on your Java background and skills. It includes several libraries and APIs for data normalization, designing, training, testing, storing and reloading a network. Further it integrates with other popular deep learning Python tools like Keras and TensorFlow.
Slides:
Objective of the presentation:
In this session attendees will learn how to kick off their path into the deep learning realm with DL4J.
They will see how to approach some classification problem from scratch, using real world datasets.
The talk is intended for developers that aim to take their first steps into the deep learning world. We will explore some basic concept about Neural Networks and see how to quickly design a Multilayer perceptron (MLP) and a Convolutional Neural Network (CNN).
Hopefully the audience will leave the session knowing how to introduce some form of deep learning tasks in their current or future development work.
Attendee pre-requisites - If none, enter "N/A":
N/A
