https://github.com/thesofakillers/dl1-labs

Code for the Deep Learning 1 course assignments, Fall 2021 edition
https://github.com/thesofakillers/dl1-labs

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Code for the Deep Learning 1 course assignments, Fall 2021 edition

• Host: GitHub
• URL: https://github.com/thesofakillers/dl1-labs
• Owner: thesofakillers
• Created: 2021-11-08T17:42:23.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2022-10-16T16:49:47.000Z (over 3 years ago)
• Last Synced: 2025-01-17T10:43:41.834Z (over 1 year ago)
• Language: TeX
• Homepage:
• Size: 6.69 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Deep Learning 1 Course - Practicals

This repository contains the code part of the three assignments of the Deep Learning 1 course, Fall 2021 edition.

I am omitting my University name for searchability reasons. My MSc university can be found on my LinkedIn or CV.

## Assignments

More details for each assignment can be found in the [assignment pdfs](./pdfs/).

For a brief overview, refer to the following:

1. Assignment 1: MLPs and Backpropagation. The following is implemented:

    - Differentiable Cross Entropy in NumPy

    - Differentiable Softmax in NumPy

    - Differentiable ReLU in NumPy

    - Differentiable Linear Layer in NumPy

    - A Multi-Layer Perceptron (MLP) in NumPy

    - An MLP in PyTorch

    - Training and Evaluation of both MLPs on CIFAR10

2. Assignment 2: CNNS, RNNs, and GNNs. The following is implemented:

    - Part 1: CNNs

        - Building blocks of a convolutional neural network in NumPy

            - Zero padding in NumPy

            - Differentiable convolution in NumPy 

            - Differentiable Max Pooling in NumPy

        - Training and evaluation of a number of torchvision models (ResNet-{18,34}, VGG-11, DenseNet-121)

    - Part 2: RNNs

        - LSTM in PyTorch, using only nn.Parameter and non-linear activation functions

        - Training and evaluation of generative LSTM Language Model on books.

    - Part 3: GNNs

        - Implementation of Graph Convolutional Neural Networks trained and evaluated on molecule data.

3. Assignment 3: Variational Autoencoders

    - Implementation of a Convolutional Variational Autoencoder in PyTorch

    - Training and Evaluation on FashionMNIST generation.