Other Material

• Data Science/Python Introduction Handbook
• ETH Deep Learning Course taught in the Fall Semester, also uses Python but with a much more intensive mathematical grounding and less focus on images.
• EPFL Deep Learning Course taught in the Spring Semester by Francois Fleuret, uses Python and PyTorch covers theoretical topics and more advanced research topics with a number of applications and code.
• FastAI Deep Learning Course and Part 2 for a very practically focused introduction to Deep Learning using the Python skills developed in QBI.
• Deep Learning for Self-Driving Cars at MIT open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application
• Reproducible Research
• Coursera Course
• Course and Tools in R
• Performance Computing Courses
• High Performance Computing for Science and Engineering (HPCSE) I
• Programming Massively Parallel Processors with CUDA
• Introduction to Machine Learning (EPFL)

Additional Lectures from Previous Years

Tutorial: Python, Notebooks and Scikit

• Lecture slides
• Handout

Roads from Aerial Images

Javier Montoya / Computer Vision / ScopeM

• Slides
• KNIME Exercises
• KNIME Workflow
• IPython Notebook
• Python Data
• IPython Notebook

Introduction to Deep Learning / Machine Learning

Presented by Aurelien Lucchi in Data Analytics Lab in D-INFK at ETHZ

• Slides