Let's play with MNIST! Generate digits with Convolutional Variational Autoencoders

This post is dedicated to the development of a Flask web application capable of drawing digits through the use of a generative model. This model is obtained by training a convolutional variational autoencoder on the MNIST dataset of handwritten digits using Keras+Tensorflow.

Personality detection using BERT

In this post I show how to leverage BERT, a transformer-based language representation model, in order to identify the personality type of users based on their writing style and the content of their posts, according to the Myers-Briggs indicator (MBTI).

Play with BERT! Text classification using Huggingface and Tensorflow

In what follows, I'll show how to fine-tune a BERT classifier, using Huggingface and Keras+Tensorflow, for dealing with two different text classification problems. The first consists in detecting the sentiment (*negative* or *positive*) of a movie review, while the second is related to the classification of a comment based on different types of toxicity, such as *toxic*, *severe toxic*, *obscene*, *threat*, *insult* and *identity hate*.

Emotion detection from speech using Bi-directional LSTM networks and attention mechanism in Keras

This post is dedicated to the development of an artificial intelligence application capable of identifying the emotions expressed through the voice in spoken language. The classification model focuses on seven different emotions (*anger*, *boredom*, *disgust*, *fear*, *happiness*, *sadness*, *neutral*) and is enhanced with the attention mechanism.

What breed is that puppy? Dog breed classification using Convolutional Neural Networks and Transfer Learning in Keras

In what follows, I'll show how to build a dog breed classifier based on Convolutional Neural Networks, which focuses on two particular breeds: Chihuahua and Pug. In order to cope with the small amount of traning data, the model exploits three main techniques: real time data augmentation, Transfer Learning and fine tuning.