Deep Learning For Humans
Artificial intelligence has made its way into the everyday lives of almost everyone, be it through self-driving cars, communication with smart speakers, or face detection on smartphone cameras. One of the driving concepts behind this progress is deep learning. Deep learning is based on powerful enhancements of artificial neural networks, which have been used in the machine learning community for a long time.
By trying to replicate the functionality of the human brain, deep neural nets are even able to outperform human decision-making in areas such as image or speech recognition.
Deep learning methods have the potential to support R&D processes in the pharmaceutical industry. They may be used to optimise clinical trials, e.g. by predicting which patients are at risk of dropping out or by automating data collection. Further, they may help to detect tumours on MRI scans and facilitate medical decision-making by first learning and then predicting diagnoses.
In this workshop, we will provide a hands-on introduction to deep learning. After a brief and informal overview of deep learning and the underlying machine learning concepts, the participants will dive into a prepared example analysis. There will be opportunities to extend or modify this example at each participant’s own pace to deepen their understanding. Tasks and questions will guide the participant through the process of applying deep neural nets on a potential clinical use case. The participants will encounter further classic machine learning concepts such as feature engineering, model training and model assessment. We will facilitate individual discussions with multiple trainers.
Previous knowledge of deep learning, machine learning or the Python language is not required. The workshop addresses beginners and advanced learners alike. All are welcome to listen.
- Pre-registration is required for active participation
- Participants will use their own computers, but no software installation is necessary
– Christoph Bergen, HMS Analytical Software
– Dr. Eike Nicklas, HMS Analytical Software