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
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Updated
Jul 14, 2019 - Python
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
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%.
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.
Implementing and fine-tuning BERT for sentiment analysis, paraphrase detection, and semantic textual similarity tasks. Includes code, data, and detailed results.
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