opencampus.sh Machine Learning Program
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opencampus.sh Machine Learning Program
Course Kick-Off
How do I choose a course?
FAQ
Courses
Einführung in Data Science und maschinelles Lernen
Machine Learning with TensorFlow
Deep Learning from Scratch
Deep Learning for Computer Vision
Requirements for a Certificate of Achievement or ECTS
Preparation
Week 1 - General Introduction
Week 2 - Foundations of Convolutional Neural Networks
Week 3 - Convolution Model Application
Week 4 - Residual Networks
Week 5 - Transfer Learning
Week 6 - Detection Algorithms
Week 7 - Project Checkpoint | Image Segmentation
Week 8 - Face Recognition
Week 9 - Art Generation with Neural Style Transfer
Week 10 - CNN Bonus
Week 11 - Final Presentation of the Projects
Transformers for Natural Language Processing and Beyond
Generative Adversarial Networks
Machine Learning für die Medizin
Events
Coding.Waterkant 2022
Prototyping Week
Projects
Requirements
Possible Projects
Past Projects
Project Template
Additional Resourses
Glossary
Coursera
Selecting the Optimizer
Choosing the Learning Rate
Learning Linear Algebra
Learning Python
Support Vector Machines
ML Statistics
Tools
Git
RStudio
Google Colab
Zoom
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Deep Learning for Computer Vision
Opencampus Course about Deep Learning based on various Coursera Courses
The Deep Learning for Computer Vision course continue the journey into neural network going into more details about Convolutional Neural Networks (CNN). This course is intended as a follow-up after the Deep Learning from Scratch course and aims at understanding and recreating complexing network architecture for fascinating and challenging projects.

The Objective of the Course

The aim of this course is to reach a deep level of understanding about how CNN works and why they are so powerful. During the course you will have to complete assignments which will give you an insight about what can be done, for example object detection, face recognition, neural style transfer.
During the course, we will have weekly discussion to deepen our understanding of the subject and time to work on your own project.

Requirements and Motivation

We assume you have knowledge about python, linear algebra and neural network. Ideally, you took the first course Deep Learning from Scratch (or a similar course) and you have done a small project. Having some experience is extremely beneficial in order to be able to keep up with the homeworks and the discussion, otherwise it tend to require a quite large amount of time.
The estimated time is around 5 to 10 hours a week distibuted between watching videos, doing homeworks and working on your project. Try to allocate enough time to manage to get through the whole course. If in doubt, ask us for advice.

The Project

Groups of students will be formed to work on a project. The project idea can come from any of the students, can be picked from a template, proposed from us or can be the continuation of a project from the last semesters. The project is needed in order to finish the course, and a final presentation will be given in the last week of the course.
For more information about the projects, check out the projects section and the sub-pages about requirements, possible and past projects!

The Course Material

The course will be held weekly and will constitute of an online session of 1 hour and a half. The material and slides for each session and will be uploaded on each week's page after the class.

ECTS

For further details about Certificates and ECTS please refer to the following page:
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Week 12 - Presentation of Final Projects, Part II
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Requirements for a Certificate of Achievement or ECTS
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The Objective of the Course
Requirements and Motivation
The Project
The Course Material
ECTS