SteliosKliafas

coursera-gan-specialization

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

Deeplearning.ai-GAN-Specialization-Generative-Adversarial-Networks

This repository contains my full work and notes on Deeplearning.ai GAN Specialization (Generative Adversarial Networks)

django-react-redux-task-app

A simple CRUD app to get comfortable with Django-React-Redux

Finetuning-Transformer-Models-Movie-Plot-Generation-Classification

Used the Netflix Movie & TV Shows dataset to finetune the GPT-2 model for Text Generation, while also finetuning the BERT model for classification to evaluate and compare the model’s accuracy on the GPT-2 generated text and the original

Generating-Deep-Fake-Images-with-DCGAN

In this project we propose the challenge to generate realistic people faces. This is one of the most controversial applications of GANs, as they recently achieved incredibly good results which raised many concerns mostly related to ethical and privacy aspects (thispersondoesnotexists). To achieve this goal, we propose the implementation of the DCGAN model. We then propose a comparison between the results obtained by trying different configurations of the model highlighting the strengths and weaknesses of each one. The dataset that we used is the ‘Labelled Faces in the Wild (LFW)’ dataset, which contains more than 13,000 images of faces collected from the web. Although this dataset is typically used for tasks related to image recognition, we found that the images suited well for the scope of our project. Furthermore, most implementations revolving around face generation use the ‘CelebA’ dataset and achieve decent results, but there are not many examples on different datasets, thus, we decided to experiment and test the performance of the DCGAN model on the LFW dataset.

HopfieldDRQN

Embedding Modern Continuous Hopfield Networks (Ramsauer et al., 2020) in the DRQN algorithm

INM705-Project-Capecchi---Kliafas

numpy_python_ann

The point of this task is to develop a multi-layer neural network for classification using Python and Numpy: Implement sigmoid and relu layers (with forward and backward pass) Implement a softmax output layer Implement a fully parameterizable neural network (number and types of layers, number of units) Implement an optimizer (e.g. SGD or Adam) and a stopping criterion of your choosing Train your Neural Network using backpropagation

shortest_path_algorithms

This repository contains implementations of algorithms that find the shortest path from a point a to point b located at a maze

SteliosKliafas/HopfieldDRQN

Embedding Modern Continuous Hopfield Networks (Ramsauer et al., 2020) in the DRQN algorithm

SteliosKliafas/coursera-gan-specialization

Programming assignments and quizzes from all courses within the GANs specialization offered by deeplearning.ai

SteliosKliafas/Deeplearning.ai-GAN-Specialization-Generative-Adversarial-Networks

This repository contains my full work and notes on Deeplearning.ai GAN Specialization (Generative Adversarial Networks)

SteliosKliafas/django-react-redux-task-app

A simple CRUD app to get comfortable with Django-React-Redux

SteliosKliafas/Finetuning-Transformer-Models-Movie-Plot-Generation-Classification

Used the Netflix Movie & TV Shows dataset to finetune the GPT-2 model for Text Generation, while also finetuning the BERT model for classification to evaluate and compare the model’s accuracy on the GPT-2 generated text and the original

SteliosKliafas/Generating-Deep-Fake-Images-with-DCGAN

In this project we propose the challenge to generate realistic people faces. This is one of the most controversial applications of GANs, as they recently achieved incredibly good results which raised many concerns mostly related to ethical and privacy aspects (thispersondoesnotexists). To achieve this goal, we propose the implementation of the DCGAN model. We then propose a comparison between the results obtained by trying different configurations of the model highlighting the strengths and weaknesses of each one. The dataset that we used is the ‘Labelled Faces in the Wild (LFW)’ dataset, which contains more than 13,000 images of faces collected from the web. Although this dataset is typically used for tasks related to image recognition, we found that the images suited well for the scope of our project. Furthermore, most implementations revolving around face generation use the ‘CelebA’ dataset and achieve decent results, but there are not many examples on different datasets, thus, we decided to experiment and test the performance of the DCGAN model on the LFW dataset.

SteliosKliafas/INM705-Project-Capecchi---Kliafas

SteliosKliafas/numpy_python_ann

The point of this task is to develop a multi-layer neural network for classification using Python and Numpy: Implement sigmoid and relu layers (with forward and backward pass) Implement a softmax output layer Implement a fully parameterizable neural network (number and types of layers, number of units) Implement an optimizer (e.g. SGD or Adam) and a stopping criterion of your choosing Train your Neural Network using backpropagation

SteliosKliafas/shortest_path_algorithms

This repository contains implementations of algorithms that find the shortest path from a point a to point b located at a maze

SteliosKliafas/INM706_DL_Sequence_Analysis

SteliosKliafas/PokerJ

SteliosKliafas/SteliosKliafas

Config files for my GitHub profile.

SteliosKliafas/TicTacToe

Local TicTacToe Game