scigeek72's Stars
jessicayung/machine-learning-nd
Udacity's Machine Learning Nanodegree project files and notes.
ottonello/notebooks
Jupyter notebooks for experimentation and graphs generation for my blog posts
lazyprogrammer/data-science-blogs
A curated list of data science blogs
lazyprogrammer/machine_learning_examples
A collection of machine learning examples and tutorials.
udacity/ud120-projects
Starter project code for students taking Udacity ud120
disq/HighlightWhitespaces
Highlight whitespaces in code. Separate colors for tabs and spaces.
benanne/kaggle-galaxies
Winning solution for the Galaxy Challenge on Kaggle (http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge)
udacity/machine-learning
Content for Udacity's Machine Learning curriculum
tensorflow/tensorflow
An Open Source Machine Learning Framework for Everyone
apache/mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
GeospatialPython/pyshp
This library reads and writes ESRI Shapefiles in pure Python.
cs109/content
Official content for Harvard CS109
cs109/2015
Public material for CS109
rstudio/shiny_example
Example shiny app
emanuele/kaggle_pbr
My best submission to the Kaggle competition "Predicting a Biological Response", ranked 17th over 711 teams.
h2oai/h2o-2
Please visit https://github.com/h2oai/h2o-3 for latest H2O
ecpolley/SuperLearnerExtra
Additional functions for the SuperLearner R package. A collection of mostly wrapper functions that might be useful for the SuperLearner but are not general enough to be placed in the main package.
DistrictDataLabs/03-mineralytics
toddwschneider/agency-loan-level
Loan-level analysis of Fannie Mae and Freddie Mac data
mnielsen/neural-networks-and-deep-learning
Code samples for my book "Neural Networks and Deep Learning"
soulmachine/machine-learning-cheat-sheet
Classical equations and diagrams in machine learning
donnemartin/data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
tidyverse/ggplot2
An implementation of the Grammar of Graphics in R
spark-mooc/mooc-setup
Information for setting up for the BerkeleyX Spark Intro MOOC, and lab assignments for the course
rasbt/pattern_classification
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
DistrictDataLabs/science-bookclub
Generating the next read for our book club- with Data Science!