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Schedule of Deep learning in Insurance (Speaker: Robin Van Oirbeek) (7.50 CPD)

Deep learning in Insurance (Speaker: Robin Van Oirbeek) (7.50 CPD)

Schedule of Deep learning in insurance

Day 1, Thursday 26 September
17:00 - 19:30 Module 1 : A comprehensive introduction to Deep Learning By VAN OIRBEEK Robin
Day 2, Thursday 3 October
17:00 - 19:30 Module 2: A deep dive into the important technical details and introduction to how to fine tune a Deep Learning model By VAN OIRBEEK Robin
Day 3, Thursday 10 October
17:00 - 19:30 Module 3: Application of the Deep Learning methodology on insurance data By VAN OIRBEEK Robin
  1. From 17:00 to 19:30

    Module 1 : A comprehensive introduction to Deep Learning

    By VAN OIRBEEK Robin

    The first part of the proposed training would provide a comprehensive yet concise introduction to the Deep Learning (DL), detailing the necessary components of any DL model. After this introduction, every attendant should be able to get the basic structure of any DL model, as well as how to assess the quality and general capabilities of any DL model. Links to useful external sources will be provided in a generous manner, such that the attendant can deepen his/her understanding of DL autonomously after the training.

  2. From 17:00 to 19:30

    Module 2: A deep dive into the important technical details and introduction to how to fine tune a Deep Learning model

    By VAN OIRBEEK Robin

    The second part is a follow-up of the first part, and we will basically pick up where we ended during the first part. Here we will be first looking at the algorithmic details of the most commonly used optimization techniques used for DL models, followed by discussing on how to fine tune a DL model. Note that fine tuning a DL model can make a big difference, due to the non-convexity of the function that needs to be optimized. Hence, this module introduces key concepts for the attendants to evolve to more advanced users of DL models.

  3. From 17:00 to 19:30

    Module 3: Application of the Deep Learning methodology on insurance data

    By VAN OIRBEEK Robin

     The third part consists of applying the DL methodology on some real insurance data, which is achieved by gently going through the R or Python code that is necessary to fit these impressive models. Guidance will also be provided to set up yourself the computational framework in your corporate environment that is able to handle DL models. Note that prior knowledge of R and/or Python is an undeniable strength, yet not an absolute requirement to appreciate the last part of the training, as the code will be discussed in full detail. As such, no stone will be left unturned, and the attendants will be provided with the necessary information to fit their favorite DL model on the data of his/her choice.

Register

Prices

Ticket type Price
Members € 375.00
Non-members € 600.00

If you are a PHD student, please contact us for a customised fee.