These are some of my projects:
Designed for kids (in Spanish) using Scratch (a block-based visual programming language). The goal is to help a diver clean the ocean, picking up plastic garbage.
NLP/transfer learning to classify literary categories of books. Technologies involved: Python (Transformers, Pytorch, Pandas, Scikit-learn, NumPy), Google BigQuery.
122000+ wikipedia spanish articles to create a language model (next word prediction) that could be later used for transfer learning. Technologies involved: Python (Fastai2).
Identifying type-color combination for 5 types and 6 colors of clothing’ images using a convolutional neural network (CNN). Technologies involved: Python (Fastai).
Identifying 42 characters of ‘The Simpsons’ using a convolutional neural network (CNN) backbone and a fully connected head with a single hidden layer as a classifier. The output is the predicted probability for each of the categories. Technologies involved: Python (Fastai).
Script that classifies tweets in 13 different emotions. ‘Master’ branch: a model using a random forest as a classifier. ‘Neural network’ branch: the model is built with a Long Short Term Memory (LSTM) neural network. Technologies involved: Python (Pandas, Numpy, Scikit-learn, Nltk, tensorflow-keras, Matplotlib).
Script to evaluate reviews about movies, classifying them with positive or negative polarities. ‘Master’ branch: a model built with Support Vector Machine. ‘Naive_bayes’ branch: a model with a class that returns the result of a voting process between Naive Bayes, Multinomial Naive Bayes and Bernoulli Naive Bayes classifiers. Technologies involved: Python (Numpy, Scikit-learn, Nltk).