README

This repository cointains application of Machine and Deep Learning techniques on images and texts.
Major Python's Machine Learning Libraries such as:

• NumPy
• Scipy
• Pandas
• SkLearn
• Matplotlib

and others have been used.
Deep Learning models have been built by means of TensorFlow.

Images

Two tasks have been addressed: Multi-Class Classification and Anomaly Detection.

Multi-Class Classification

The following models have been used:

• Convlutional Neural Networks
• SupportVector Machines
• AdaBoost with Decision Trees as weak learners.
• K-Nearest Neighbour
• Categorical Naive Bayes

A classification on data at lower dimesnionality by means of PCA has been provided too.

Anomaly Detection

The following models have been used:

• One-Class SupportVector Machines
• Variational AutoEncoder

Texts

Two tasks have been addressed: Multi-Class Classification (in particular, a Sentiment Analisys) and Anomaly Detection.

Multi-Class Classification

The following models have been used:

• SupportVector Machines
• AdaBoost with Decision Trees as weak learners.
• K-Nearest Neighbour
• Multinomial Naive Bayes
• Sequential Neural Networks
• Convlutional Neural Networks

Anomaly Detection

The following models have been used:

• One-Class SupportVector Machines
• AutoEncoder