Topic outline

• General

  Collapse all Expand all
  • DAT112 Neural Network Programming (Apr. 2023)

    This six-week class will introduce neural network programming concepts, theory and techniques. The class material will begin at an introductory level, intended for those with no experience with neural networks, eventually covering intermediate-to-advanced concepts. The programming language will be Python 3.10; experience with Python programming will be assumed. The Keras neural network framework will be used for neural network programming; no experience with Keras will be expected.  Lectures will be 11:00am - 12:00pm, Tuesday and Thursdays.  There will be no lectures on May 30 and June 1.
    Format: In-person, in the SciNet teaching room (661 University Ave., suite 1140A).  All lectures will be recorded.

    Teacher: Erik Spence

    Start date: 25 Apr 2023

    End date: 8 Jun 2023

    Data Science Credits: 16

    Events:

    Neural Network Programming Lecture - Thursday, 27 April, 10:04 AM » 11:04 AM

    Neural Network Programming Lecture - Tuesday, 2 May, 11:00 AM » 12:00 PM

    Neural Network Programming Lecture - Thursday, 4 May, 10:04 AM » 11:04 AM

    A full list of course events can be found here.
  • 

    Forum

    Announcements Forum

• Lectures
  • • 1. Introduction
      • intro_I.pdf
      • lecture1_code.tar.gz
  • • 2. Neural networks
      • intro_II.pdf
      • lecture2_code.tar.gz
      • mnist.pkl.gz
  • • 3. Frameworks
      • frameworks.pdf
      • lecture3_code.tar.gz
  • • 4. Convolutional neural networks
      • CNNs.pdf
      • lecture4_code.tar.gz
  • 

    File

    5. Image classification File
  • • 6. Variational autoencoders
      • MNIST_VAE.mpg
      • mnist_vae.py
      • VAEs.pdf
  • • 7. Generative adversarial networks
      • GANs.pdf
      • MNIST_gan.mpg
      • MNIST_gan.py
  • • 8. Reinforcement learning
      • q_cartpole_player.py
      • RL.pdf
  • • 9. Graph neural networks
      • GNNs.pdf
      • lecture9_code.tar.gz
  • • 10. Recurrent neural networks
      • lecture10_code.tar.gz
      • RNNs.pdf
  • • 11. Sequence-to-sequence networks
      • lecture11_code.tar.gz
      • S2S.pdf
  • • 12. Attention networks
      • Attention.pdf
      • lecture12_code.tar.gz

• Recordings
  • 

    File

    1. Introduction File
  • 

    File

    2. Neural networks File
  • 

    File

    3. Frameworks File
  • 

    File

    4. Convolutional neural networks File
  • 

    File

    5. Image classification File
  • 

    File

    6. Variational autoencoders File
  • 

    File

    7. Generative adversarial networks File
  • 

    File

    8. Reinforcement learning File
  • 

    File

    9. Graph neural networks File
  • 

    File

    10. Recurrent neural networks File
  • 

    File

    11. Sequence-to-sequence networks File
  • 

    File

    12. Attention File

• Topic 3
  • 

    Assignment

    Assignment 1

    Opened: Thursday, 4 May 2023, 11:30 AM

    Due: Thursday, 11 May 2023, 11:59 PM
  • 

    Assignment

    Assignment 2

    Opened: Thursday, 18 May 2023, 11:30 AM

    Due: Thursday, 25 May 2023, 11:59 PM
  • 

    Assignment

    Assignment 3

    Opened: Thursday, 8 June 2023, 11:30 AM

    Due: Thursday, 15 June 2023, 11:59 PM