Deep Learning
This one-week Deep Learning course covers theoretical and practical aspects, state-of-the-art deep learning architectures and application examples.
Practical information:
Georganiseerd door:
The lectures will introduce to students the fundamental building blocks of deep learning methods and the weaknesses and strengths of the different architectures. Students will learn how to tailor a model for a particular application. During tutorials students practice the theory using exercises and have the opportunity to ask for additional explanation for those parts of the material perceived as more difficult. Computer lab sessions aim at making the material come alive and train students in how the methods learnt in class can actually be applied to data. The lab sessions are meant to work on the assignments, such that the students automatically keep up with the material.
After studying this course you will:
- Introduction to Deep Learning (High-level definitions of fundamental concepts and first examples)
- Deep Learning components (gradient descent models, loss functions, avoiding over-fitting, introducing asymmetry)
- Feed forward neural networks
- Convolutional neural networks
- Embeddings (pre-trained embeddings, examples of pre-trained models, e.g., Word2Vec)
- Recurrent neural networks
- Long-short term memory units
- Advanced architectures (Densely connected networks, Adaptive structural learning)
Teacher / speaker
Eran Raviv holds a PhD in econometrics from Erasmus University Rotterdam, a master’s degree in applied statistics from Tel Aviv University and a second master’s degree in quantitative finance from Erasmus University Rotterdam. His research has been published in high impact peer-reviewed journals.
In 2013 he joined APG Asset-Management as a senior quantitative investment strategist. Since spending two years in that role, he has served as a data scientist working on various innovative projects across Asset-Management organization, focusing on deep learning methodologies and NLP applications. He currently holds an expert-researcher position at the Digitalization department, and teaches Deep Learning for the master program in Business Data Science, Amsterdam.
