Pinned Repositories
BiLSTM-vs-BERT-in-feature-extraction-for-Neural-Dependency-Parsing
Completed as part of the "Natural Language Processing" course, this project employs the ArcEager parsing algorithm. Implementation is carried out using PyTorch and the Hugging Face library for utilizing pretrained BERT models.
Blind-Face-Restoration-and-Upscaling
This project, created for the "Vision and Cognitive Systems" course, employs generative networks with landmarks as priors to reconstruct full-size and small-size blind faces.
Estimation-and-Analysis-of-Mutual-Information-in-a-Recurrent-Neural-Network
This project, developed as part of the "Information Theory and Inference" exam, aims to use different discrete and continous estimators to calculate the mutual information between layers of a RNN trained to perform a cognitive task.
Exercises-for-Advanced-Statistics-for-Physics-Analysis
This repository presents a variety of exercises in R language covering both basic and advanced statistical topics.
Exercises-for-Machine-Learning
In this repository, you'll find a set of Python exercises focused on fundamental machine learning concepts using scikit-learn library.
Exercises-for-Physical-Models-for-Living-Systems
This repository contains some numerical analysis regarding physical models applied to both Ecology and Neuroscience, written in Python.
Exercises_for_Numerical_Methods_in_Soft_Matter
This repository contains the reports of numerical simulations of monte carlo, markov chains and molecular dynamics methods.
Hierarchical-Mergers-of-Binary-Black-Holes
Developed for "Laboratory of Computational Physics Mod. A", this project employs graphical and machine learning tools to identify the features with the greatest impact on the fate of binary black holes in Nuclear Star Clusters (NSC), Globular Clusters (GC), and Young Star Clusters (YSC).
Reports-for-Stochastic-Methods-for-Finance
This repository comprises a collection of reports covering various topics in advanced financial mathematics. Accompanying the reports are Visual Basic for Applications scripts used for data analysis on Excel.
Standard-GAN-vs-WGAN-for-Image-Colorization
Developed for the "Neural Networks and Deep Learning" course, this project utilizes PyTorch for GAN implementation.
zoppellarielena's Repositories
zoppellarielena/Blind-Face-Restoration-and-Upscaling
This project, created for the "Vision and Cognitive Systems" course, employs generative networks with landmarks as priors to reconstruct full-size and small-size blind faces.
zoppellarielena/Estimation-and-Analysis-of-Mutual-Information-in-a-Recurrent-Neural-Network
This project, developed as part of the "Information Theory and Inference" exam, aims to use different discrete and continous estimators to calculate the mutual information between layers of a RNN trained to perform a cognitive task.
zoppellarielena/Hierarchical-Mergers-of-Binary-Black-Holes
Developed for "Laboratory of Computational Physics Mod. A", this project employs graphical and machine learning tools to identify the features with the greatest impact on the fate of binary black holes in Nuclear Star Clusters (NSC), Globular Clusters (GC), and Young Star Clusters (YSC).
zoppellarielena/Reports-for-Stochastic-Methods-for-Finance
This repository comprises a collection of reports covering various topics in advanced financial mathematics. Accompanying the reports are Visual Basic for Applications scripts used for data analysis on Excel.
zoppellarielena/Standard-GAN-vs-WGAN-for-Image-Colorization
Developed for the "Neural Networks and Deep Learning" course, this project utilizes PyTorch for GAN implementation.
zoppellarielena/BiLSTM-vs-BERT-in-feature-extraction-for-Neural-Dependency-Parsing
Completed as part of the "Natural Language Processing" course, this project employs the ArcEager parsing algorithm. Implementation is carried out using PyTorch and the Hugging Face library for utilizing pretrained BERT models.
zoppellarielena/Exercises-for-Advanced-Statistics-for-Physics-Analysis
This repository presents a variety of exercises in R language covering both basic and advanced statistical topics.
zoppellarielena/Exercises-for-Machine-Learning
In this repository, you'll find a set of Python exercises focused on fundamental machine learning concepts using scikit-learn library.
zoppellarielena/Exercises-for-Physical-Models-for-Living-Systems
This repository contains some numerical analysis regarding physical models applied to both Ecology and Neuroscience, written in Python.
zoppellarielena/Exercises_for_Numerical_Methods_in_Soft_Matter
This repository contains the reports of numerical simulations of monte carlo, markov chains and molecular dynamics methods.
zoppellarielena/LaboratoryOfComputationalPhysics_Y4
Repo for Laboratory of Computational Physics, year 21-22
zoppellarielena/LCP_projects_Y4
Projects for Laboratory of Computational Physics, mod. A, Year 4
zoppellarielena/MAPD-B
zoppellarielena/Mini-Batch-K-means-on-RCV1-dataset-using-Dask
Developed for "Management and Analysis of Physics Dataset Mod. B," this project uses Dask and CloudVeneto VMs to handle a massive 250GB dataset. Clustering on 800k RCV1 articles involves dataset reduction by macrocategory and also implementing cosine similarity for improved clustering, as suggested by Natural Language Processing principles.
zoppellarielena/mlreview_notebooks
Notebooks for "A high bias low-variance introduction to Machine Learning for physicists."
zoppellarielena/Multinomial-Naive-Bayes-for-Fake-News-Classification
Developed for the Advanced Statistics for Physics Analysis course, the implementation is carried out using the R language.
zoppellarielena/Paper-Presentation-for-Natural-Language-Processing
This presentation, conducted for the "Natural Language Processing" course, delves into the paper's content, which addresses the challenge of generating images for a story using a text-to-image framework. The paper can be accessed at https://arxiv.org/abs/2210.08465.
zoppellarielena/Rainfall-runoff-modeling-using-Deep-Learning
Completed for the "Laboratory of Computational Physics Mod. B" under the supervision of Professor Carlo Albert. The project utilizes Keras in TensorFlow for implementation.
zoppellarielena/zoppellarielena