Deep learning arguably is the hottest trend in data analysis. It has pushed the boundaries in Big Data Analytics and Artificial Intelligence and has been outperforming the state-of-the-art in many applications from various domains, for instance, object classification in images, information retrieval including web search, natural language processing tasks such as automatic translation, and bioinformatics.
Not only large players such as Google and Facebook, but also more and more small and medium-sized companies are successfully applying deep learning techniques to solve commercially relevant problems in a broad variety of application areas as diverse as drug design, customer relation management and mortgage risk estimation.
By completing the course you will be able to set up and use basic deep learning techniques. In particular, you will learn how to use deep convolutional neural networks and recurrent neural networks for image and text analysis tasks. You will also be acquainted with a number of advanced tools and will become familiar with using appropriate computational resources to train and apply deep learning models.
The course will also teach the theoretical foundations of deep neural networks, which will provide the understanding necessary for adapting and successfully applying deep learning in your own applications.
Deep learning refers to machine learning algorithms that process data in multiple stages, each stage working on a different representation of the data. These representations are learned and allow for analyzing data on different levels of abstraction.
This course will bring you to the forefront of deep learning, introducing the foundations as well as the newest tools and methods in this emerging field.