Deep learning is used successfully in many data science applications, such as image processing, text processing, and fraud detection. KNIME offers an integration to the Keras libraries for deep learning, combining the codeless ease of use of KNIME Analytics Platform with the extensive coverage of deep learning paradigms by the Keras libraries.
Though codeless, implementing deep learning networks still requires orientation to distinguish between the learning paradigms, the feedforward multilayer architectures, the networks for sequential data, the encoding required for text data, the convolutional layers for image data, and so on.
This course will provide you with the basic orientation in the world of deep learning and the skills to assemble, train, and apply different deep learning modules.
This is an instructor-led course consisting of four, 75 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: Exercise walk though and final Q&A