This repo includes my lab teaching tutorials and material for the CIT690E Deep Learning course. It is a postgraduate course that aims to build student experience with Deep Learning topics including the common architectures in different applications among the concerns while training, ... etc.
I believe building the intuition while learning Deep Learning is quite crucial in order to have a transferable knowledge to other different applications. Therefore, during the labs, that is what I was mainly concerned about in addition to common issues like data imbalance and similar issues.
- Lab 1: course environment
- Lab 2: Tensor Operations
- Lab 3: Building Neural Networks with PyTorch
- Lab 4: Batch Normalization and Dropout
- Lab 5: pre-trained models [VGG16]
- Lab 6: Object Detection
- Lab 7: Image Captioning
- Lab 8: Attention and DETR
- Lab 9: BERT
- Lab 10: Generative Models [CycleGAN]
Some of the material were inspired by the previous material by Ahmed Hosny as well as the textbook: Modern Computer Vision with PyTorch
- Github Repo: CIT690E-Deep-Learning-Labs
- Email: ammarsherif90 [at] gmail [dot] com