How subhan-ullah-alfaqeer/generative-adversarial-networks-gans-specialization works
This repository is a two-week GAN learning course delivered through interactive notebooks and lecture PDFs. The apparent learner workflow is to open the weekly materials and work through exercises, spanning PyTorch introduction, a first GAN, pretrained-GAN inputs, an assignment, and video generation. Notebook contents and execution flow were not available, and there is no README, so relationships between lessons and generated results are not inferred.
Main entry points: Course-1-Week-1-Optional-Labs-1-Intro-to-PyTorch.ipynb PyTorch Intro · Course-1-Week-1-Optional-Labs-2-Inputs-to-a-pre-trained-GAN.ipynb Pretrained GAN Inputs · Course-1-Week-1-Practice-Labs-1-Your-First-GAN.ipynb First GAN · Course-1-Week-1.pdf Week 1 Lecture
Components 8
Week 1
- PyTorch Intro
01 Build Basic Generative Adversarial Networks (GANs)/01_Week/01 Assingment/Course-1-Week-1-Optional-Labs-1-Intro-to-PyTorch.ipynb - Pretrained GAN Inputs
01 Build Basic Generative Adversarial Networks (GANs)/01_Week/01 Assingment/Course-1-Week-1-Optional-Labs-2-Inputs-to-a-pre-trained-GAN.ipynb - First GAN
01 Build Basic Generative Adversarial Networks (GANs)/01_Week/01 Assingment/Course-1-Week-1-Practice-Labs-1-Your-First-GAN.ipynb - Week 1 Lecture
01 Build Basic Generative Adversarial Networks (GANs)/01_Week/Lecture Files/Course-1-Week-1.pdf
Week 2
- Week 2 Assignment
01 Build Basic Generative Adversarial Networks (GANs)/02_Week/Assingment/C1_W2_Assignment.ipynb - Video Generation
01 Build Basic Generative Adversarial Networks (GANs)/02_Week/Assingment/C_1_2_Optional_Labs_1_Video_Generation.ipynb - Week 2 Lecture
01 Build Basic Generative Adversarial Networks (GANs)/02_Week/Lecture Files/Course-1-Week-2.pdf
Other components
- Learner
Connections 7
- Learner → PyTorch Intro: opens
- Learner → Pretrained GAN Inputs: opens
- Learner → First GAN: opens
- Learner → Week 1 Lecture: reads
- Learner → Week 2 Assignment: opens
- Learner → Video Generation: opens
- Learner → Week 2 Lecture: reads