Pinned Repositories
Bayesian-Machine-Learning
Deriving Variational Inference and Expectation Maximization Algorithms From Scratch
cc22tt
Source files for community contribution repo for EDAV Fall 2022 Tues/Thurs
Cornell-Machine-Learning
Practice from My Machine Learning Certificate from Cornell
katlass
Config files for my GitHub profile.
Machine-Learning
A Collection of Data Science and Machine Learning Projects Utilizing Scikit-Learn, TensorFlow, and R for Predictive Modeling, Time Series Analysis, and Statistical Methods.
Misc-Columbia-Projects
Space Optimized Computer Vision, Performance of Deep Learning Systems, Algorithms, Spark/MapReduce, R Bookdown with D3 Interactive
Natural-Language-Processing
CNN, LSTM, GPT and Other NLP Models with PyTorch and Transformers
Repeat-Sales-Model-using-Distributed-Computing
Federal Reserve: Forecasting Corporate Bond Returns With a Repeat Sales Model on 72 Distinct Billion-Item Matrices ~ 1TB
Self-Supervised-Wrist-Gait-Characterization
Johnson & Johnson (contract): Signal Processing and Machine Learning for Gait Analysis on Terabytes of Wrist Sensor Data
Web-Scraping-SEC-Data
Web scraping commercial paper and negotiable certificates of deposit data from the SEC EDGAR public website https://www.sec.gov/edgar/searchedgar/legacy/companysearch.html
katlass's Repositories
katlass/Cornell-Machine-Learning
Practice from My Machine Learning Certificate from Cornell
katlass/Machine-Learning
A Collection of Data Science and Machine Learning Projects Utilizing Scikit-Learn, TensorFlow, and R for Predictive Modeling, Time Series Analysis, and Statistical Methods.
katlass/Natural-Language-Processing
CNN, LSTM, GPT and Other NLP Models with PyTorch and Transformers
katlass/Repeat-Sales-Model-using-Distributed-Computing
Federal Reserve: Forecasting Corporate Bond Returns With a Repeat Sales Model on 72 Distinct Billion-Item Matrices ~ 1TB
katlass/Web-Scraping-SEC-Data
Web scraping commercial paper and negotiable certificates of deposit data from the SEC EDGAR public website https://www.sec.gov/edgar/searchedgar/legacy/companysearch.html
katlass/Bayesian-Machine-Learning
Deriving Variational Inference and Expectation Maximization Algorithms From Scratch
katlass/cc22tt
Source files for community contribution repo for EDAV Fall 2022 Tues/Thurs
katlass/katlass
Config files for my GitHub profile.
katlass/media_disinformation
Data visualizations exploring media disinformation, including interactive D3 javascript
katlass/Misc-Columbia-Projects
Space Optimized Computer Vision, Performance of Deep Learning Systems, Algorithms, Spark/MapReduce, R Bookdown with D3 Interactive
katlass/PolicyPlotter-User-Interface-for-Plotting-Board-Charts
Policy Plotter – R Shiny Application: Solved a core problem facing the Federal Reserve Board by independently developing a plotting application to vastly increase the efficiency of creating charts/exhibits for The Federal Open Market Committee (FOMC). This committee determines monetary policy for the entire United States. I automated the process of chart creation which draws focus away from data and code integrity.
katlass/Self-Supervised-Wrist-Gait-Characterization
Johnson & Johnson (contract): Signal Processing and Machine Learning for Gait Analysis on Terabytes of Wrist Sensor Data
katlass/Space-Optimized-Computer-Vision
Space optimized CNN developed through synchronous distributed training, weight pruning, and quantization in Vertex AI on GCP
katlass/String-Similarity-Custom-Functions
Text Classification: Fast, custom string similarity functions in Python mapping lambda functions to NumPy arrays, assigning issuers to one of 10,000 classes.
katlass/Umass-Senior-Project-2019
The purpose of my application was to solve a problem many businesses (small businesses in particular) face. They do not know how much to produce, where to price, how much to spend on advertising and many other questions. Eden’s purpose was to answer these questions for them easily and with no technical acumen required by the user. Eden would model supply and demand equations using ordinary least squares (OLS) regression on the user’s data to form the best fitting supply and demand equations possible. The best fit was to be ensured by regressing each variable against demand or supply, determine the best shape via the highest adjusted R2, and then do an OLS regression and simplistically tell the user what the results mean. Eden would attempt multiple shapes like linear, logarithmic, cubic, quadratic, and inverse. The user interface would be easy to navigate and user-friendly.