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auserj's Projects

phyloseq icon phyloseq

phyloseq is a set of classes, wrappers, and tools (in R) to make it easier to import, store, and analyze phylogenetic sequencing data; and to reproducibly share that data and analysis with others. See the phyloseq front page:

pillow icon pillow

The friendly PIL fork (Python Imaging Library)

pip icon pip

The PyPA recommended tool for installing Python packages

pipeline icon pipeline

PipelineAI: The Standard Runtime For Every Real-Time Machine Learning and AI Prediction in the Enterprise

pipenv icon pipenv

Python Development Workflow for Humans.

pistache icon pistache

A high-performance REST Toolkit written in C++

pix2pix-tensorflow icon pix2pix-tensorflow

Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets https://phillipi.github.io/pix2pix/

pix2pixhd icon pix2pixhd

Synthesizing and manipulating 2048x1024 images with conditional GANs

pixel-cnn icon pixel-cnn

Python3 / Tensorflow implementation of PixelCNN++, as described in "PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications"

polyglot icon polyglot

Multilingual text (NLP) processing toolkit

polylabel icon polylabel

A fast algorithm for finding the pole of inaccessibility of a polygon (in JavaScript and C++)

pomegranate icon pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

ponyge2 icon ponyge2

PonyGE2: grammatical evolution and variants in Python

portfolio-optimization icon portfolio-optimization

Built a portfolio optimization algorithm to maximize expected returns on a combination of stocks using live feeds and historical data from yahoo finance using regression models, matrix transformations and principal component analysis. Designed a model to predict the performance of the portfolio based on multiple factors such as market returns, volatility, GDP and inflation

ppsspp icon ppsspp

A PSP emulator for Android, Windows, Mac and Linux, written in C++. Want to contribute? Join us on Discord at https://discord.gg/5NJB6dD or in #ppsspp on freenode (IRC) or just send pull requests / issues. For discussion use the forums on ppsspp.org.

practical-machine-learning-with-python icon practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

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