The conditions to be met in order to receive a Certificate of Achievement and the ECTS are:

1. Missing the weekly session maximum 2 times.

2. Presenting and uploading the project with the needed documentation.

Attendance:

Projects:

Check the Projects section to learn more about the projects.

Coursera:

Each weekly session is complemented with the videos and homework from the Coursera courses. Going through the video and doing the assignment allows you to learn and understand each session, so it is required for the course.

However, Coursera is indipendent from us and the completion of the Coursera assignment is NOT needed for the Opencampus Certificate. Completing all assignment will give you the Coursera Certificates (which is different)

This week you will..

• have a short talk about how to structure your idea for the project

• start to form the groups for the project

• discuss about advantages and limitations of CNN

• take a look at the first homework

Slides

Until next week you should

• check additional material on convolutional models

• finish the second homework of the first week

Every group will present their project. All details about timing and format will be discussed in the classroom and updated later here.

This week you will

• get an introduction to the course and possible projects,

• have a round of presentation

• get an overview of the resources for the course

• brainstorm ideas for the projects

Slides

Until next week you should

• Check the videos of the first week of the Convolutional Neural Network Course

• Finish the first homework (only the first for this week)

• Write down and bring specific question (only if you have some)

This week you will..

• Discuss about transfer learning

• Have a small internal discussion about your project and present us some insights

Learning Resources

Until next week you should

• Check out the videos for the

• Finish the homework on

This week you will..

• Discuss about residual networks

• Discuss about training practices

Learning Resources

Until next week you should

• Check additional material on Transfer Learning

• finish the homework on

This week you will..

• Follow a peer review process of the projects

• Discuss the main difference with U-Net

• Discuss about segmentations

Learning Resources

Until next week you should

• Check the videos of the

• finish the homework on

This week you will..

• Check out the face recognition code

Learning Resources

Until next week you should

• Finish the homework on

This week you will..

• discuss about CNN, advantages and limitation after the course

• think about what can happens in the future

• hear about transformer in computer vision

Learning Resources

Until next week you should

• Prepare the presentation!

This week you will..

• Discuss the car detection algorithms

• Think about other detection/localization applications

Learning Resources

Until next week you should

• Prepare a small presentation about the state of your project.

• Finish the homework about

The presentation should contain:

• goal of the project

• state of the art (was it already done? If yes, how? If no, why?)

• your approach

Before the course start, you should be familiar with:

• our Opencampus.sh Mattermost Chat

• the Coursera platform and the Deep Learning Specialization

• the framework

What would be very beneficial for this course (but not mandatory, you will learn it anyway during the course):

• knowledge of and packages

• knowing how to work with images in python

• knowing what a convolution is

This week you will..

• Discuss about neural network and art generation

• Discuss about cost functions

Learning Resources

Check out Phil Wang's Repositories for more cool projects:

Until next week you should

• Work on your project - no fixed homework this week!

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.

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:

This week you will..

• Discuss about CNN models

• Finalize groups and set objective for the project

• Check out the homework

Learning Resources

Until next week you should

• check out the videos for the

• finish the homework on (only this one)