|
|
|
||||||
|
||||||
|
|
|
|
Deep learning (DL) pertains to the field of artificial intelligence and is great at extracting and mastering the often highly non linear patterns of a given process, whatever this process might be. The only main requirement is the availability of a large amount of data that describes the behaviour of the process under different conditions and a truckload of computational power. With data collection becoming cheaper and computational power still following Moore’s law, fitting DL models that produce extremely useful predictions has become a practical reality.
While this family of models is broad, one particular architecture has reshaped the field of text analysis: the transformer. Transformers were originally introduced to overcome the limitations of earlier neural networks when dealing with sequential data such as text, where long range dependencies and contextual meaning matter. Their ability to process entire sequences in parallel and to model relationships between all words at once made them uniquely suited for language tasks.
Large Language Models (LLMs) are essentially very large transformer networks trained on massive text corpora. They represent a natural continuation of deep learning, but with capabilities—reasoning over text, summarising documents, generating explanations—that go far beyond what earlier DL architectures could achieve. Understanding LLMs therefore benefits from first understanding the deep learning principles on which they are built.
The main purpose of this web session is to get the participants acquainted with DL models, and applications on text analysis will help achieve this. To this end, a healthy mix between theory and practice will be provided, however, it is important to note that some time will be spent to go through the theoretical foundations of neural networks and hence DL, as the inner workings of these models are a bit different from the ones of the classic statistical models.
The practical sessions will make use of Keras, Tensorflow and R(Studio).
Early-bird discount is available for bookings made by 31 August 2026. |
|
|
|
Coming soon... |
|
Fit4AI compact | 2/3 June 2026
Machine Learning & Generative AI: A Hands-On Guide to Actuarial Practice | 8/9 June 2026 in Munich
DORA – The New EU Regulation on Digital Operational Resilience | 12 June 2026
Explore our website for more information and discover all our upcoming events. For more insights, updates, and a bit of actuarial fun, feel free to follow us on LinkedIn! |