This repository contains an assortment of my projects in machine learning. Here's a brief overview of some of what you'll find here:
The ml_from_scratch folder contains notebooks where I implement various machine learning algorithms from scratch using only NumPy. Each notebook contains detailed documentation, mathematical explanations, and demos to experiment with the algorithms.
I built these projects to practice the skill of taking mathematical formulas and turning them into vectorized code. As such, I did not review anyone else's code related to these algorithms but rather studied their mathematical formulations.
Currently, the algorithms I have implemented are:
A configurable fully-connected neural network that can be used for regression or classification tasks. This is an all-purpose algorithm that can be applied to many tasks like word embeddings, time series predictions, or multiclass classification (especially when using tabular data).
Key algorithms used include: backpropagation and gradient descent, cross entropy loss, L2 regularization, sigmoid and ReLU activations, forward propagation, normalization, parameter initialization, and a training and validation loop.
Results on sample data:
| Classification | Regression |
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A vectorized implementation of the K-Means clustering algorithm that groups an unlabeled dataset into
Key techniques used include: Euclidean distance, cosine similarity, vector normalization (L2), and a lot of plotting code for animated visualization.
| Generate color palettes from images |
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| Fit to any numerical data |
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A logistic regression model for binary classification using gradient descent to optimize the model's parameters. Uses any number of input features.
Key algorithms include: binary cross entropy loss (log loss), sigmoid, and threshold selection using the inverse of sigmoid.
Training progress on a sample dataset, showing how the decision boundary is updated through gradient descent:
A linear regression model trained using gradient descent with optional early stopping. Uses any number of input features (i.e., multilinear regression).
Key algorithms used include: mean squared error loss, model training through gradient descent.
Results from a multilinear regression demo: notice that y_hat (color gradient) closely matches y (vertical axis).
In my dominant color extraction notebook, I use the K-Means algorithm to estimate the main color for an image, which I then apply to the task of detecting a vehicle's color given an image of that vehicle. I used this technique in my vehicle specs project as part of a feature engineering pipeline to augment a dataset with vehicle color information.
A comparison of different Gradient Boosting Machine (GBM) implementations on a classification dataset, including visualizations.
In this notebook, adapted from a tutorial by Usman Malik, I implement a method for extractive text summarizing and use it to summarize Wikipedia articles.
I created a sort-of API to generate images using the Craiyon text-to-image model, available at: https://www.craiyon.com/
Note: I last updated this notebook in fall 2022; the website may have changed since then and the web requests might not function as intended.
I took a computational linguistics course and compiled my work on course assignments into a single notebook. In this notebook, I compute precision and recall for various NLP algorithms, compare categories and word frequency distributions across a corpus, classify sentiment, use part-of-speech tagging, predict categories using synsets from WordNet, and use LDA to visualize topics, compute the number of topics using perplexity and coherence, and use a lot of RegEx, web scraping, NLTK, and topic modeling with Gensim.
