Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. This course will cover the basics of deep learning by using a hands-on approach.
Jordi Vitrià, Departament de Matemàtiques i Informàtica de la UB.
- First Semester (September, 2020 - January, 2021)
- Face-to-face Lectures: Thursday 15:00h-16:00h
- Location: Aula T1, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona.
- Introduction | Slides
- McKinsey DataLab (15/10/20, 2:00 to 4:00 p.m.)
- Basic Concepts: Machine Learning and Optimization | Slides
- SGD and Automatic Differentiation | Slides
- Tensorflow and Keras | Notebook
- Convolutional Neural Networks | Slides | Notebook1 | Notebook2
- Contrastive & Self-Supervised Learning | Slides | Notebook
- Deep Sequential Models | Slides | Notebook
- Deep Representations of Words | Slides | Notebook1 | Notebook2
- Attention and Context-based Embeddings | Slides
- Non Supervised Learning | Slides