{"id":20652463,"url":"https://github.com/thesofakillers/dl1-labs","last_synced_at":"2026-03-07T16:33:21.486Z","repository":{"id":70961854,"uuid":"425936243","full_name":"thesofakillers/dl1-labs","owner":"thesofakillers","description":"Code for the Deep Learning 1 course assignments, Fall 2021 edition","archived":false,"fork":false,"pushed_at":"2022-10-16T16:49:47.000Z","size":7018,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-17T10:43:41.834Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"TeX","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thesofakillers.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-11-08T17:42:23.000Z","updated_at":"2022-10-16T16:48:16.000Z","dependencies_parsed_at":"2024-02-12T20:30:35.858Z","dependency_job_id":null,"html_url":"https://github.com/thesofakillers/dl1-labs","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thesofakillers%2Fdl1-labs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thesofakillers%2Fdl1-labs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thesofakillers%2Fdl1-labs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thesofakillers%2Fdl1-labs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thesofakillers","download_url":"https://codeload.github.com/thesofakillers/dl1-labs/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242752233,"owners_count":20179474,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-16T17:35:05.140Z","updated_at":"2026-03-07T16:33:21.437Z","avatar_url":"https://github.com/thesofakillers.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Deep Learning 1 Course - Practicals\n\nThis repository contains the code part of the three assignments of the Deep Learning 1 course, Fall 2021 edition.\nI am omitting my University name for searchability reasons. My MSc university can be found on my LinkedIn or CV.\n\n## Assignments\n\nMore details for each assignment can be found in the [assignment pdfs](./pdfs/).\nFor a brief overview, refer to the following:\n\n1. Assignment 1: MLPs and Backpropagation. The following is implemented:\n    - Differentiable Cross Entropy in NumPy\n    - Differentiable Softmax in NumPy\n    - Differentiable ReLU in NumPy\n    - Differentiable Linear Layer in NumPy\n    - A Multi-Layer Perceptron (MLP) in NumPy\n    - An MLP in PyTorch\n    - Training and Evaluation of both MLPs on CIFAR10\n2. Assignment 2: CNNS, RNNs, and GNNs. The following is implemented:\n    - Part 1: CNNs\n        - Building blocks of a convolutional neural network in NumPy\n            - Zero padding in NumPy\n            - Differentiable convolution in NumPy \n            - Differentiable Max Pooling in NumPy\n        - Training and evaluation of a number of torchvision models (ResNet-{18,34}, VGG-11, DenseNet-121)\n    - Part 2: RNNs\n        - LSTM in PyTorch, using only nn.Parameter and non-linear activation functions\n        - Training and evaluation of generative LSTM Language Model on books.\n    - Part 3: GNNs\n        - Implementation of Graph Convolutional Neural Networks trained and evaluated on molecule data.\n3. Assignment 3: Variational Autoencoders\n    - Implementation of a Convolutional Variational Autoencoder in PyTorch\n    - Training and Evaluation on FashionMNIST generation.\n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthesofakillers%2Fdl1-labs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthesofakillers%2Fdl1-labs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthesofakillers%2Fdl1-labs/lists"}