Online Course: [L4-DL] Introduction to Deep Learning - Online

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.

Course Content:

  • 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


thank you everyone who attended our deep learning course last week!

Please find below the answers to the open questions from our Q&A session:

  1. Will I get the same results if I use a seed in the Keras Network Learner node?
    The seed in the Keras Network Learner node ensures that if the node is re-executed always the same subsets are used for the different epochs. That doesn’t mean automatically that you will get always the same results, as the there are more random variables used when training a neural network, which influence the trained network, e.g. the initialized weights for each layer.

  2. I have a GPU. How can I check whether or not it is used to train the network?
    To do so you can check the GPU utilisation. On windows this is possible via Performance Tab in the Task Manager. Another option is to use the command nvidia-smi in the console.