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.
- [L1-DS] KNIME Analytics Platform for Data Scientists: Basics - March 30
- [L1-DW] KNIME Analytics Platform for Data Wranglers: Basics - March 30
- [L3-DC] KNIME Server Course: Productionizing and Collaboration - March 30
- [L4-TP] Introduction to Text Processing - March 30
- [L2-DS] KNIME Analytics Platform for Data Scientists: Advanced - March 31
- [L2-DW] KNIME Analytics Platform for Data Wranglers: Advanced - March 31
- [L3-PC] KNIME Server Course: Productionizing and Collaboration - March 31
- [L4-BD] Introduction to Big Data with KNIME Analytics Platform - March 31
- [L4-ML] Introduction to Machine Learning Algorithms - March 31
- [L4-PR] The Power of Random: Using Perturbation Experiments to Improve Model Accuracy and Interpretation - March 31
You must be competent in using KNIME Analytics Platform. We strongly recommend you be at the level of 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.
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/download.