Diana Low's repositories
advanced-shiny
Shiny tips & tricks for improving your apps and solving common problems
awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
footballstats
Exploring football statistics data and APIs
nMyo
nMyo package for the https://foo-lab.com/ @ NUS/GIS implemented with RShiny written by Diana Low and Efthimios Motakis
odas_tools
Supporting functions for ODAS https://github.com/introlab/odas
SPLINTER
SPLINTER provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments.
time-block-planner
Script to generate my version of Cal Newport's time-block planner pages.
coursera_machine_learning
Coursework for Andrew Ng's Machine Learning
datacamp-community-tutorials
Tutorials for DataCamp (www.datacamp.com)
DDiGGER
Data-Driven Grouping for Gene Expression in R
deeplearning
My code for Google's Deep Learning course
dianalow.github.io
Playing around with Jekyll for http://dianalow.github.io
dlaicourse
Notebooks for learning deep learning
EE123-Labs
Digital Signal Processing | Spring 2016 with Prof. Miki Lustig
introtodeeplearning
Lab Materials for MIT 6.S191: Introduction to Deep Learning
knitr
A general-purpose tool for dynamic report generation in R
lego_evolution
Datacamp exercises of interest
neural-networks-and-deep-learning
Code samples for my book "Neural Networks and Deep Learning"
pymmw
Pythonic mmWave Toolbox for TI's IWR Radar Sensors
quant_trading_echan_book
Notes for the Book Quantitative Trading by Ernie Chan
RiceFOC
Rice University's Fundamentals of Computing specialization
Spark-and-Python-for-Big-Data-with-PySpark
Udemy course
Speaker-Identification-Python
Speaker Identification System using python_speech_features library
SpeakerRecognition
Implementing speaker recognition using Python (GMM-UBM)
ssviz-web
R shiny implementation of the ssviz package
tensorflow
Computation using data flow graphs for scalable machine learning