Projects: MachineLearning
Pixelate Images and Cluster Colors Using K-Means
Pixelate images using vanilla Python and then reduce the number of colors using Scikit-Learn K-Means clustering. The algorithm starts from the raw image with optional clustering, packaged in a Python class with supporting methods. Pydantic is used for parameter validation.
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Bird Song Classifier with Vision Transformer
A continuation of the Bird Song Classifier with Machine Learning project by utilizing a vision transformer to classify bird species based on bird songs/calls. Python programming language is used with HuggingFace Transformers and Tensorflow as the primary machine learning libraries.
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Bird Song Classifier with Transfer Learning
A continuation of the Bird Song Classifier with Machine Learning project by utilizing transfer learning techniques to classify bird species based on bird songs/calls. Python programming language is used with HuggingFace Transformers and Tensorflow as the primary machine learning libraries.
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Fine Tune LLM for Grammatical Classification
Fine tuned RoBERTa using Tensorflow on the CoLA dataset to create a grammatical acceptability classifier. Experimented with different hyperparameters and achieved results on par with the original published paper. All data cleaning, analysis, and model building were conducted using the Python programming language.
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Bird Song Classifier with Machine Learning
Utilizing various machine learning algorithms (traditional, shallow neural networks, and deep neural networks) to classify bird species based on bird songs/calls. Data was sourced from the BirdCLEF 2023 kaggle competition, and all data cleaning, analysis, and model building were conducted using the Python programming language.
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