2024

Aligning an LLM with Direct Preference Optimization

• Notebook example of fine-tuning a 1.6B parameter model from Stablity AI
• Uses a dataset of human preference data released by Anthropic
• Trains a new model that generates text more aligned with what humans prefer, rather than just modeling language based on what it saw during the unsupervised portion of training.

Documents Manager

• Web application for summarizing PDF documents.
• Generates summaries for documents after upload.
• Documents stored in S3, metadata and summaries stored in DynamoDB.
• Pages for viewing existing documents and uploading new documents.

2023

Effective Data Handling with Custom PyTorch Dataset Classes

• Writing custom PyTorch Dataset classes to handle data during training.
• Separating data processing code from training code, improving readability.
• Enabling training on datasets that don’t fit into memory.
• Utilizing transfer learning with a vision transformer model (vit_b_16).
• GitHub repo containing two examples can be viewed here.
  • Titanic dataset example
  • Food-101 dataset example

2021

Sentiment Classification with BERT

• Create a sentiment classifier trained on a dataset of restaurant reviews from Yelp.
• Use a compact, pre-trained BERT model presented by Google Research in this paper as a base. This model is much smaller than the original BERT model, allowing it to easily fit in the 1 GB RAM limit of Streamlit Sharing, which I use to host the model in a web app.
• Tokenize the reviews, send them through the BERT model, and retrieve the output vectors in the [CLS] position.
• Train a classifer to take these output vectors and classify them as positive or negative.
• Create a web app that serves the model.

Deploy a Machine Learning Model with FastAPI

• Create a RESTful API that serves a machine learning model.
• Version 1 allows users to pass inputs as path variables.
• Version 2 allows users to pass inputs as a JSON payload.
• FastAPI automatically generates interactive documentation for the API using Swagger UI and Redocly.
• GitHub repo for this project can be viewed here.

Image Classifier with MobileNet

• Multi-page web application that allows users to classify an image of an object.
• Provides two methods of image input. The user can upload an image or provide a URL to an image online.
• The web app processes the image for input into MobileNet and captions the image with the output prediction.
• GitHub repo for this project can be viewed here.
• Model is trained on 1,000 classes.

Create a Machine Learnining App with Streamlit

• Web application developed with Streamlit to classify Iris flowers.
• User can customize model input values using sliders in the sidebar.
• Visualizes confidence of the model’s prediction with Plotly.
• Visualizes the user input compared to other datapoints on a PCA plot.
• GitHub repo for this project can be viewed here.
• Article showing how I built this application here.
• Article was included in Streamlit’s Weekly Roundup

2020

Near Real-Time Financial News Dataset with AWS Lambda

• Create an automated serverless function with AWS Lambda to scrape and parse CNBC Finance articles daily.
• Use a deployment package with Python libraries not included in the AWS Lambda Python 3.8 environment.
• Save text files in an S3 bucket organized by year, month, and day, making it easily queryable from AWS Athena.

Iris Flower Classifier Web Application

• Web application developed with TensorFlow.js that trains a feed-forward neural network within client’s browser.
• Enables the client to run live inference on custom inputs.
• Visualizes confidence of the model’s predictions using Plotly.js.
• GitHub repo for the project can be viewed here.

COVID-19 Dashboard

• Web application for visualizing COVID-19 case density across the U.S. and Florida
• Developed with Python webframework Flask.
• Interactive visualizations created with Python library Bokeh.
• Retrieves data from Johns Hopkins repository.
• Ran on a VM with Google Cloud Platform.

Deploying a Portfolio Investment Strategy in the Cloud

• Deploy a Global Minimum Variance portfolio investment strategy with daily rebalances on Alpaca brokerage with a serverless function on AWS.

Disease Classification from Blood Samples

• Predict if a patient has Hepatitis, Fibrosis, Cirrhosis, or no disease given lab readings from their blood sample.
• Uses an ensemble of models: dense neural network, gradient boosted decision tree, and random forest.
• Keras, XGBoost, and scikit-learn.

Plotting stock prices in Python

• Plotting OHLC data using candlesticks

2019

2018

Tital Survival Prediction

• Predict if a Titanic passenger will survive.
• Data cleaning and preparation.
• Feature engineering.
• Use a neural network developed with TensorFlow.

MNIST Digit Classification in R

• Classify an image of a hand-written digit with classes 0-9.
• Utilize Keras in R.

2017

Breast Tumor Classification

Bank Direct Marketing Campaign

Notes

Machine Learning Notes

• Explanations and demos of various machine learning topics and techniques.

Finance Notes

• Retrieving and streaming data with financial APIs and websockets
• Portfolio construction and risk analysis
• Options pricing analysis
• Backtesting investment strategies
• Implementing investment strategies with Alpaca
• Streaming live trading data to AWS

Certifications

• Introduction to Deep Learning
• Neural Networks and Deep Learning
• Structuring Machine Learning Projects
• Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
• Convolutional Neural Networks
• Bayesian Statistics: From Concept to Data Analysis
• Bayesian Methods for Machine Learning
• Data Visualization
• Google Cloud Platform Fundamentals: Core Infrastructure
• How to Win a Data Science Competition: Learn from Top Kagglers
• Introduction to Portfolio Construction and Analysis with Python
• Advanced Portfolio Construction and Analysis with Python