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• Select section General

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  General

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  • Select activity Course Description

    DAT112 Neural Network Programming (Apr 2024)

    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.
    

    Classes will be held, April 23 - May 30, Tuesdays and Thursdays, 11:00am - 12:00pm.

    Format: Virtual

    Teacher: Erik Spence

    Start date: 23 Apr. 2024

    End date: 30 May 2024

    Data Science Credits: 16

    Events:

    DAT112: Lecture 1 - Tuesday, 23 April, 11:00 AM » 12:00 PM

    DAT112: Lecture 2 - Thursday, 25 April, 11:00 AM » 12:00 PM

    DAT112: Lecture 3 - Tuesday, 30 April, 11:00 AM » 12:00 PM

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

    

    Announcements Forum
• Select section Lectures

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  Lectures

  • Select activity 1. Introduction

    • 1. Introduction
      • intro_I.pdf
      • lecture1_code.tar.gz
  • Select activity 2. Neural networks

    • 2. Neural networks
      • intro_II.pdf
      • lecture2_code.tar.gz
      • mnist.pkl.gz
  • Select activity 3. Frameworks

    • 3. Frameworks
      • frameworks.pdf
      • lecture3_code.tar.gz
  • Select activity 4. Convolutional neural networks

    • 4. Convolutional neural networks
      • CNNs.pdf
      • lecture4_code.tar.gz
  • Select activity 5. Image classification

    

    5. Image classification File
  • Select activity 6. Variational autoencoders

    • 6. Variational autoencoders
      • MNIST_VAE.mpg
      • mnist_vae.py
      • VAEs.pdf
  • Select activity 7. Generative adversarial networks

    • 7. Generative adversarial networks
      • GANs.pdf
      • MNIST_gan.mpg
      • MNIST_gan.py
  • Select activity 8. Diffusion models

    • 8. Diffusion models
      • DM.pdf
      • lecture8_code.tar.gz
  • Select activity 9. Graph neural networks

    • 9. Graph neural networks
      • GNNs.pdf
      • lecture9_code.tar.gz
  • Select activity 10. Recurrent neural networks

    • 10. Recurrent neural networks
      • lecture10_code.tar.gz
      • RNNs.pdf
  • Select activity 11. Sequence-to-sequence networks

    • 11. Sequence-to-sequence networks
      • lecture11_code.tar.gz
      • S2S.pdf
  • Select activity 12. Attention networks

    • 12. Attention networks
      • Attention.pdf
      • lecture12_code.tar.gz
• Select section Recordings

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  Recordings

  • Select activity 1. Introduction

    

    1. Introduction File
  • Select activity 2. Neural networks

    

    2. Neural networks File
  • Select activity 3. Frameworks

    

    3. Frameworks File
  • Select activity 4. Convolutional neural networks

    

    4. Convolutional neural networks File
  • Select activity 5. Image classification

    

    5. Image classification File
  • Select activity 6. Variational autoencoders

    

    6. Variational autoencoders File
  • Select activity 7. Generative adversarial networks

    

    7. Generative adversarial networks File
  • Select activity 8. Diffusion models

    

    8. Diffusion models File
  • Select activity 9. Graph neural networks

    

    9. Graph neural networks File
  • Select activity 10. Recurrent neural networks

    

    10. Recurrent neural networks File
  • Select activity 11. Sequence-to-sequence networks

    

    11. Sequence-to-sequence networks File
  • Select activity 12. Attention networks

    

    12. Attention networks File
• Select section Assignments

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  Assignments

  • Select activity Assignment 1

    

    Assignment 1

    Opened: Thursday, 2 May 2024, 11:30 AM

    Due: Thursday, 9 May 2024, 11:59 PM
  • Select activity Assignment 2

    

    Assignment 2

    Opened: Wednesday, 15 May 2024, 12:00 AM

    Due: Thursday, 23 May 2024, 11:59 PM
  • Select activity Assignment 3

    

    Assignment 3

    Opened: Tuesday, 28 May 2024, 11:00 AM

    Due: Tuesday, 4 June 2024, 11:59 PM