[ECE NTUA] Neural Networks

Lab Projects of the Neural Networks course held by ECE - NTUA during the Fall Semester of 2021-2022.

1st Lab Exercise: Supervised Learning

Experimenting with various classifiers (Naive Bayes, K-Nearest Neighbours, Support Vector Machines, Logistic Regression, MLP) on supervised classification tasks in real-life datasets (UCI, Kaggle). Applied data preprocessing (Variance Thresholding, Scaling, PCA) and hyperparameter optimization techniques (GridSearch, Cross-validation, Optuna) to maximize classification metrics (Accuracy, F1-score, Recall), in the form of classic machine learning pipelines.

2nd Lab Exercise: Unsupervised Learning

Created a movie Recommending System based on context, using Natural Language Processing techniques (tf-idf, pretrained word embeddings), as well as based on genre, through topological and semantic mapping with Self-Organized Maps (SOMs).

3rd Lab Exercise: Deep Learning

Implemented an Image Captioning System for images of a flick30k dataset variation, based on Convolutional and Recurrent Neural Networks and evaluated with the BLUE metric.