Deep Learning

Theory and applications for Computer Vision

046003, Spring 2019

Tomorrow 14/03/19 class starts at 09:00

Description

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 winter school 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.


Course Staff


Registration

  • Technion undergrad students: UG (link)
  • Technion grad students: Graduate Studies Secretariat (at your faculty)
  • Free listeners (or students who wish to take the course with out academic credit): REGISTRATION IS CLOSE!


Prerequisites

Knowledge of the following is required:

  • Machine learning 046195 or 236501 or 236757 (students from W18 semester are welcome).
  • Proficiency in Python.
  • Linear algebra.
  • Basic Probability and Statistics.

Free listeners please fill the following registration form.


Class Time and Location

March 10-14, Sunday-Thursday 9:30am-12:20pm.

Mayer 280, EE building, The Technion.


Grading Policy

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" (בחירה פקולטית) points).

Course Project (%70):

The project will involve implementing and training Neural Networks for a given computer vision task. Will be done in pairs. To be submitted within 3 weeks after the course end.

Course Exam (%30):

The exam does not require any preparation for those who attended all lectures.

  • Moed A- Friday 15/03/2019, 9:30, Mayer 165.
  • Moed B- Monday 25/03/2019, 18:30.


Course Project

You can find instructions regarding the final project in link.

How to fetch data from kaggle by using google colaboratory, you can find in link.

You can find PyTorch 60 minutes blitz tutorial in link.

Contact Information

Registration to the course is available through the links above (there is no need to write us for registration)