Deep Learning Workshop - Methods, Applications and Hands-on Experience

The next workshop will be held in April 2017. For more information, please visit our: 2017 Workshop-Webpage

From April 5th until April the 7th, a Deep Learning-workshop will take place at CAMP. We will introduce the participants in some related topics, like:

Fundamentals of Deep Learning

We will introduce the workshop with an overview of the contents and how everything ties together. In this first lecture we will start from the basics of neural networks to build the foundations for the more advanced topics. The main mathematical notations will be introduced here and we will provide you with a short refresher on the necessary mathematical topics, like basics in linear algebra, derivatives and tensors. From there we will analyze simple shallow networks and learn the concepts of stochastic gradient descent, forward and backpropagation on those simple examples. Further we will look at the typical hyper parameters learning rate, momentum, batch size, etc. This will give you the tools and understanding for the following more advanced topics.

Domain Adaptation in Deep Learning

All learning algorithms, including autoencoders and CNNs, are trained on a specific dataset and thus develop a bias towards it. Domain adaptation (DA) deals with taking models trained on a source domain and transferring the knowledge into a new target domain. In this lecture, we will introduce you to the main facets of domain adaptation, take a closer look at dataset bias and delve into approaches to adapt autoencoders and neural networks.

Introduction to MatConvNet

MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for MATLAB. It is designed to give simplicity and efficiency in developing new network layers and architectures for CNNs. It supports both CPU and GPU computations with a C++ back-end for the basic network operations. We will give an introduction, as well as explanations of basic concepts and development with MatConvNet.

Introduction to TensorFlow

Google recently released TensorFlow to the public in an effort to boost Machine Learning research and the announcement was extremely well received both from companies and universities worldwide. TensorFlow is a general-purpose framework to model, train and execute computational graphs using Python and/or C++. We will give an introduction as well as explain basic concepts of TensorFlow and its usage as a Deep Learning suite.


For detailed information, please visit our Workshop-Webpage.