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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
check additional material on convolutional models
finish the second homework of the first week Convolution Model Application
Discuss about residual networks
Discuss about training practices
Check additional material on Transfer Learning
finish the homework on Transfer Learning
Discuss about CNN models
Finalize groups and set objective for the project
Check out the homework
check out the videos for the second week of the coursera course
finish the homework on Residual Network (only this one)
Discuss the car detection algorithms
Think about other detection/localization applications
Prepare a small presentation about the state of your project.
Finish the homework about Image Segmentation using U-Net
goal of the project
state of the art (was it already done? If yes, how? If no, why?)
your approach
your dataset
your problems (if any :D )
Discuss about transfer learning
Have a small internal discussion about your project and present us some insights
Check out the videos for the third week of the coursera course
Finish the homework on Car detection with YOLO
Check out the face recognition code
Finish the homework on Art Generation with Neural Style Transfer
Every group will present their project. All details about timing and format will be discussed in the classroom and updated later here.
The conditions to be met in order to receive a Certificate of Achievement and the ECTS are:
Missing the weekly session maximum 2 times.
Presenting and uploading the project with the needed documentation.
We register automatically the attendance.
When you join the Zoom Session, please use the same name you have in the edu.opencampus.sh platform. You can change your name in the edu.opencampus.sh platform (click at the top-right on your profile photo) and in Zoom (click at the top-right of your video stream), so you should be able to use the same name during the weekly session.
If for any reason (no need to explain) you do not want to use the same name, but still need to be registered, please contact me at the beginning of the course.
Check the Projects section to learn more about the projects.
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)
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 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.
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!
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.
For further details about Certificates and ECTS please refer to the following page:
Before the course start, you should be familiar with:
the Coursera platform and the Deep Learning Specialization
the Tensorflow framework
What would be very beneficial for this course (but not mandatory, you will learn it anyway during the course):
knowledge of numpy and opencv python packages
knowing how to work with images in python
knowing what a convolution is
Follow a peer review process of the projects
Discuss the main difference with U-Net
Discuss about segmentations
Check the videos of the fourth week of the coursera course
finish the homework on Face Recognition
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
Check the videos of the first week of the Convolutional Neural Network Course
Finish the first homework Convolutional Model, Step by Step (only the first for this week)
Write down and bring specific question (only if you have some)