Deep convolutional neural networks (CNNs) have revolutionized computer vision, achieving unprecedented performance in high-level vision tasks such as classification, detection, segmentation an low-level vision tasks.
This crash course aims to give a first dive into the world of deep learning, with hands-on experience. During an intensive one-week course, students will learn to implement, train and debug their own neural networks.
We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. back-propagation), practical engineering tricks for training and fine-tuning the networks. The final assignment will involve training a large scale convolutional neural network and applying it on a practical data-set.
This course is based on Stanford's CS231n: Convolutional Neural Networks for Visual Recognition course.
Knowledge of the following is required:
Free listeners please fill the following registration form.
February 23-27, Sunday-Thursday 9:30am-12:20pm.
Meyer 815, EE building, The Technion.
The course is designed for students planning to attend all lectures and would like to dive into deep learning.
The course is worth one credit point (not included in any chain of studies, but can be regarded as a "faculty" points (בחירה פקולטית)).
Course Project (%70):
The project will involve implementing and training Neural Networks for a given computer vision task. Submission is in pairs, and the due is within 3 weeks after the course end.
Course Exam (%30):
The exam does not require any preparation for those who attended all lectures.
submission deadline: 19/04/2020 (postponed). Please submit the project to this email.
You can look for a project teammate in the following spreadsheets.
Useful tutorials:
Registration to the course is available through the links above (there is no need to write us for registration)