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.
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!