Webinar: Codeless Deep Learning for Sequential Data

This webinar on deep learning moves on from building and training a simple neural network to implementing special deep learning architectures for sequential data, called recurrent neural networks.

Sequential data is all around us. Language as a sequence of words, time series as a sequence of numerical values like stock prices or sensor data, or signals as a sequence of samples from a sound wave, to give you just a few examples. This kind of data has special requirements when it comes to deep learning architectures.

What are the goals of this webinar?
In this webinar we start with inspecting different requirements of sequential data and how deep learning models can handle them. We’ll look at different use cases, which have all been implemented without code, including demand prediction with multivariate time series and a number of text based applications.

Who is this webinar for?
We welcome anyone interested in deep learning to join us! It will be of particular benefit to data analysts, data scientists, and deep learning developers who want to take advantage of the KNIME GUI to build, train, test, and deploy deep learning networks.

Who are the speakers?
Join Kathrin Melcher, data scientist at KNIME and Rosaria Silipo, principal data scientist at KNIME and head of the Evangelism Team, who wrote the book "Codeless Deep Learning with KNIME" (https://www.knime.com/codeless-deep-learning-book), which is available via Packt Publishing.

For an introduction to deep learning and the concepts behind training models, join our "A friendly introduction to codeless deep learning" webinar on March 9.