Throughout the past decade, the use of deep learning methods is constantly rising. Many more applications in fields like image, sound and video processing, natural language processing, time series analysis, and unstructured data are based on deep learning methods.
In this course, we offer the chance to learn more about neural networks in general as well as specific deep learning units. We will start by implementing a feed-forward neural network architecture and training it with the backpropagation algorithm. We will continue to explore more recent advances in the field, such as LSTM and GRU units for sequence analysis and Convolutional Neural Networks for image processing.
This is an instructor-led course consisting of four, 90 minute online sessions run by two KNIME data scientists. Each session has an exercise for you to complete at home. The course concludes with a 15- to 30-minute wrap up session.
- Session 1: Classic Neural Networks and Introduction to KNIME Deep Learning Extensions
- Session 2: Deep Learning Case Studies and Different Design Components
- Session 3: Recurrent Neural Networks
- Session 4: Convolutional Neural Networks
- Session 5: Wrap Up and Q&A
You must be competent in using KNIME Analytics Platform. We strongly recommend you are an advanced KNIME user - for example you’ve taken a basic and advanced KNIME Analytics Platform Course and/or use KNIME on a regular basis.
You’ll receive a zoom link with your registration confirmation. Make sure you have a stable internet connection!
Sure! The sessions will be recorded and you’ll have access to each one for one month from the time the session is over.
Absolutely - fire away!
Your own laptop, ideally pre-installed with the latest version of KNIME Analytics Platform, which you can download at knime.com/downloads.
Download the latest free, open source version of KNIME here: knime.com/downloads