This is the repository for the content of inzva 2020-June Applied AI Study Group, guided by Ahmet Melek and Onur Boyar.
In the group we have worked on these subjects:
-
Frameworks: Tensorflow, Keras Functional API, Keras Sequential API, Pytorch
-
Topics: Image Classification, Image Localisation, Image Segmentation
-
Architectures - Methods: Artificial Neural Networks (Fully-Connected Neural Networks), Convolutional Neural Networks (CNN)
-
Environments: Google Colab, Jupyter Notebook (Local)
We have worked on six problems:
-
Image Classification with MNIST dataset on tensorflow, using Fully-Connected Neural Networks.
-
Image Classification with MNIST dataset on keras functional API, using Convolutional Neural Networks.
-
Image Classification with CIFAR-10 dataset on keras sequential API, using Convolutional Neural Networks.
-
Image Localisation with Kaggle Facial Keypoint Detection dataset on keras sequential API, using Convolutional Neural Networks.
-
Image Localisation with Kaggle Facial Keypoint Detection dataset on Pytorch, using Convolutional Neural Networks.
-
Image Segmentation with testing on random images from internet on Pytorch, using pretrained resnet101 model.
For all examples in Week1, we have worked on Google Colab.
In homework one, participants take an aligned hand image dataset with keypoint labels, and try to preprocess the dataset and make regression on the keypoint coordinates.