Discussions

You can download the discussions here. We will try to upload discussion material prior to their corresponding sessions. Future discussions listed below likely have broken links.

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  Discussion_01 - Environment Setup and an Intro to Pytorch, Tensors, and Tensor Operations
  tl;dr: Setting up SCC, virtual environments, and a brief intro to Pytorch, Tensors, and Tensor Operations.
  [slides]
  

  Notebook: 00_fundamentals.ipynb
  Suggested Readings:

  • SCC Resources
  • Introduction to Pytorch
  • What’s a Tensor? - Dan Fleisch
  • Pytorch Internals - Edward Z. Yang
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  Discussion_02 - Autograd, and Computational Graphs in Pytorch. Intro to Model Building in Pytorch.
  tl;dr: Fundamentals of Autograd and Computational Graphs in Pytorch. Introduction to Defining a Neural Network in Pytorch (Basics)
  
  

  Notebook: 01_autograd.ipynb
  Suggested Readings:

  • Fundamentals of Autograd
  • Overview of PyTorch Autograd Engine
  • Pytorch Internals - Edward Z. Yang
  • Builders’ guide (d2l.ai)
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  Discussion_03 - Intro to Model Training in Pytorch. (Image Classification, Text Classifcation)
  tl;dr: Model Training Code Walkthrough. Intro on how to handle Text for Deep Learning Networks.
  
  

  Notebook: 02_intro_nn_training.ipynb

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  Discussion_04 - Data Loading In PyTorch. CNNs
  tl;dr:
  
  

  Notebook: 03_intro_nn_training.ipynb

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  Discussion_05 - RNNs, LSTMs
  tl;dr:
  
  

  Notebook: RecurrentNNs.ipynb

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  Discussion_06 - Vision Transfromers (ViTs), CLIP
  tl;dr:
  
  

  Notebook: CLIP.ipynb

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  Discussion_07 - LLaVA
  tl;dr: Large Vision Language Models (LVLMs) - LLaVA, Design Choices in Vision Encoders (Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs - https://arxiv.org/abs/2401.06209)
  
  

  Notebook: LVLM.ipynb Slides: Eyes Wide Shut

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  Discussion_08 - AE, VAEs Intro
  tl;dr:
  
  

  Notebook: VAE.ipynb

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  Discussion_09 - Brief Intro to Diffusion models, Diffusers Library
  tl;dr:
  
  

  Notebook: diffusion.ipynb