Developing a well-documented repository for the Lung Nodule Detection task on the Luna16 dataset. This work is inspired by the ideas of the first-placed team at DSB2017, "grt123".

The repository includes Jupyter notebooks with deep learning examples in Python.

My notebooks on the book "Deep Learning with Python" by Francois Chollet (2018)

Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data.

Jupyter notebooks implementing Deep Learning algorithms in Keras and Tensorflow

Image Classification

This repository contains the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.

This notebook demonstrates data augmentation as showcased by Tensorflow: a technique to increase the diversity of your training set by applying random (but realistic) transformations, such as image rotation.

A quick image classifier trained with manually selected One Piece images.

This project implements deep learning models for classifying images. Using TensorFlow and Keras, it includes scripts and notebooks for training and testing neural networks on various datasets to achieve high accuracy in image categorization.

In this repository you can find the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.