b-ghimire's repositories
pytorch-CycleGAN-and-pix2pix
Image-to-image translation in PyTorch (e.g. horse2zebra, edges2cats, and more)
TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners with Latest APIs
R2CNN_FPN_Tensorflow
R2CNN: Rotational Region CNN Based on FPN (Tensorflow)
SSD-Tensorflow
Single Shot MultiBox Detector in TensorFlow
FCN.tensorflow
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)
tensorflow
Computation using data flow graphs for scalable machine learning
twitter-sentiment-analysis
R shiny web application to scrape tweets based on user-defined search keyword and perform sentiment analysis of the tweets. Sentiment analysis of tweets consists of classifying tweets into emotion classes (i.e., anger, disgust, fear, joy, sadness and surprise) and also polarity classes (i.e., negative, neutral and positive) using naïve Bayes classifier. The tweets are scraped, classified into sentiment classes and visualized in R using twitteR, sentiment and ggplot2 packages, respectively.
location-finder
Python flask web application for selecting different locations (i.e., counties) in United States based on multiple factors related to population, earnings, age, housing, education, rent, employment, health and physical activity. The different factors are standardized using linear transformation and then aggregated into a suitability index by weighting each standardized factor with user-specified importance weights. The suitability indices for each county are then ranked from most suitable to least suitable and displayed on a map. Web Application Framework: Python Flask <br> Backend: Python (NumPy, Pandas), SQLite <br> Frontend: HTML, CSS, JavaScript, jQuery, Ajax, Bootstrap, D3
data-science-mini-projects
A collection of mini-projects on data science.
python-pelican-blog
Bash shell scripts to automatically set-up, create, add content and host a Python Pelican blog.
machine-learning-R
A collection of R code for running machine learning algorithms including random forest, bagging, boosting and classification trees. The R scripts were originally developed for land-cover classification but can easily be extended and applied to other supervised classification problems.
python-data-structures-and-algorithms
Collection of data structures and algorithms in python
i.jmdist
GRASS GIS script to calculate Jeffries-Matusita distance