Implements https://arxiv.org/abs/1711.05101 AdamW optimizer, cosine learning rate scheduler and "Cyclical Learning Rates for Training Neural Networks" https://arxiv.org/abs/1506.01186 for PyTorch framework

The goal of this project is to devise an accurate CNN-based classifier able to distinguish between Cat and Dog in images where the animal is predominant.

Deep Neural Network built from scratch to tackle ML binary classification predicting Titanic survival (Kaggle Competition). Best DNN AdamW 77.03% and best RF 78.95%.

Implementing and fine-tuning BERT for sentiment analysis, paraphrase detection, and semantic textual similarity tasks. Includes code, data, and detailed results.