Edit: Use this document to ask for help during the zoom lab sessions
Zoom link: https://uu-se.zoom.us/j/69918669715

The topic of this year's computer lab is deep learning. The lab is carried out in groups of two students. To pass the course, you must pass the computer lab. No written report is required, but you need to come to the lab session, work actively on the material and complete the lab to pass. The lab is graded (U/G) based on the students presenting their solutions to the teacher at the end of the lab session. The mandatory preparatory exercises must be completed and submitted on Studium before the first scheduled lab session. If you do not complete and submit the preparatory exercises before the deadline, you will not be able to take part in the lab sessions this course instance.

Important Deadlines:

• 17th of February: Last day to sign up to a lab group

• 26th of February: Submit your solutions to the preparatory exercises

The goal of this laboratory work is to:

• Learn how to build and train a neural network
• Learn how to improve the neural network model and its training.
• Application 1: Learn how to classify hand-written digits using neural networks.
• Application 2: See how a state-of-the-art deep neural network performs at classifying real-world images
• Get a glimpse of a state-of-the-art software library (PyTorch) for deep learning.

The lab instructions and other lab material can be found at the bottom of this page. You are welcome (and recommended) to start looking at the lab material before the lab session. This will make the lab session more efficient. If you complete the lab before the lab session, you can come to the session simply to present your solutions to the teacher.

Lab groups:

Sign up to a lab group here. See deadline above.

Make sure you sign up to group corresponding to the session you wish to attend. The schedule is as follows:

Group 1-20: Mon 27/2 ON CAMPUS

Group 21-40: Tue 28/2 ON CAMPUS

Group 41-60: Wed 1/3 ON CAMPUS

Group 61-80: Thu 2/3 ON CAMPUS

Group 81-100: Fri 3/3 ZOOM

Preparatory exercises:

1. Read Chapter 6 and Section 5.4 in the SML course book.

2. Read Chapter 1 and 2 in the lab instructions. If you are new to PyTorch, have a look at the Introduction to PyTorch notebook before the lab session. Reading and running the notebook is highly recommended, since it introduces important concepts and commands that are required in the lab session. The instructions and introduction notebook can be found at the bottom of this page.

3. Read Chapter 3 in the lab instructions. Solve the preparatory exercises found in this chapter and submit your solutions on Studium. You can discuss the preparatory exercises with other students, but each student must submit their own solutions. See the deadline for submitting your solutions to the preparatory exercises on the top of this page. Submit your solutions here.

4. To be well-prepared for the lab, read through the problem formulation in section 4.1 and get familiar with the notebook mnist_onelayer.ipynb by going through section 4.1.1 (and if you have time, sections 4.1.2 - 4.1.3). This part is a recommended but not mandatory part of the lab preparations that will allow you to work more efficiently during the lab session.

Installation:

We recommend using Google Colab to work with PyTorch online. This cloud platform also optionally provides access to GPUs which might speed up some computations. Another option is to work locally. PyTorch is already installed on the Linux systems in the computer rooms where the laboratory session is scheduled. You can use these computers during the lab or bring your own computer. If you choose to use your own computer, you need to have PyTorch properly installed before the lab (or use Google Colab). The lab assistants will not be able to assist you with the installation process during the lab. Please consult the PyTorch documentation for more information about the installation procedure.

Topic	Material
Lab instructions	lab_instructions.pdf
Introduction to PyTorch	Notebook Open in colab
mnist_onelayer.ipynb	Notebook Open in colab
VGG16_classification.ipynb	Notebook Open in Colab