jptrinastic

Topic_Modeling

Topic modeling methodology using natural language processing and unsupervised clustering techniques (K-means, Gaussian mixture models, LDA and doc2vec to be implemented).

20220726_Databricks_Demo_Transfer_Learning_with_MLflow

We will go hands-on with an image classification demo using transfer learning, while leveraging MLflow to track our model experiments on Databricks

Bayesian-Analysis

Several worked examples and descriptions, with supporting code, that describe basic Bayesian Analysis based on Allen Downey's Think Bayes book.

EnergyForecasting

Kaggle---Nomad-TCO

Kaggle competition to predict TCO band gap and formation energies. Output from Light GBM model placed in top 6%.

keras-adversarial-autoencoders

Experiments with Adversarial Autoencoders using Keras

MILP_vs_Find_batt_scheduler

Uses data from the data driven battery scheduling competition; introduces battery scheduling using MILP; compares to the find optimisation method I developed for the competition

mlhub-tutorials

Tutorials to access Radiant MLHub Training Datasets

mlrose

Python package for implementing a number of Machine Learning, Randomized Optimization and SEarch algorithms.

numerical-mooc

A collaborative educational initiative in computational science and engineering.

jptrinastic/satellite-image-deep-learning

Resources for deep learning with satellite & aerial imagery

jptrinastic/20220726_Databricks_Demo_Transfer_Learning_with_MLflow

We will go hands-on with an image classification demo using transfer learning, while leveraging MLflow to track our model experiments on Databricks

jptrinastic/mlhub-tutorials

Tutorials to access Radiant MLHub Training Datasets

jptrinastic/mlrose

Python package for implementing a number of Machine Learning, Randomized Optimization and SEarch algorithms.

jptrinastic/pssa

Singular Spectrum Analysis for time series forecasting in Python

jptrinastic/simanneal

Python module for Simulated Annealing optimization

jptrinastic/random-forest-importances

Code to compute permutation and drop-column importances in Python scikit-learn random forests

jptrinastic/pymssa

Python implementation of Multivariate Singular Spectrum Analysis (MSSA)

jptrinastic/Solar_Forecasting

Comparing various time series forecasting methods using HI-SEAS Hawaii dataset in context of small training data set size.

jptrinastic/keras-adversarial-autoencoders

Experiments with Adversarial Autoencoders using Keras

jptrinastic/MILP_vs_Find_batt_scheduler

Uses data from the data driven battery scheduling competition; introduces battery scheduling using MILP; compares to the find optimisation method I developed for the competition

jptrinastic/Bayesian-Analysis

Several worked examples and descriptions, with supporting code, that describe basic Bayesian Analysis based on Allen Downey's Think Bayes book.

jptrinastic/Topic_Modeling

Topic modeling methodology using natural language processing and unsupervised clustering techniques (K-means, Gaussian mixture models, LDA and doc2vec to be implemented).

jptrinastic/Kaggle---Nomad-TCO

Kaggle competition to predict TCO band gap and formation energies. Output from Light GBM model placed in top 6%.

jptrinastic/Solar_Panel_Detection

Comparing neural networks and deep forests in ability to detect solar panels from satellite images.

jptrinastic/Phonon_Induced_Relaxation_TDDFT

Physical model to calculate the electronic relaxation rates within linear response theory using TDDFT

jptrinastic/pycel

A library for compiling excel spreadsheets to python code & visualizing them as a graph

jptrinastic/TLS_Search

jptrinastic/RAKE

A python implementation of the Rapid Automatic Keyword Extraction

jptrinastic/EnergyForecasting

jptrinastic/numerical-mooc

A collaborative educational initiative in computational science and engineering.