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a-smattering-of-nlp-in-python

A very brief introduction to Natural Language Processing programming in Python

adversarial_networks

Tensorflow implementation of deep convolutional - generative adversarial networks deployed on AWS gpu instance

alfred-github-workflow

GitHub command bar for Alfred 2

alfred-terminalfinder

Alfred workflow to open current Finder window in Terminal/iTerm and vice versa.

alfred-workflows

:metal: A collection of Alfred 2 workflows that will rock your world

alfred-workflows-1

Workflows for Alfred 2

alfredworkflows

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

bluebook

This IPython Notebook contains a quantitative pricing model created for Fast Iron in the Kaggle competition 'Blue Book for Bulldozers'. The model predicts the sale price of a particular piece of heavy equipment so that Fast Iron can create a 'Blue Book' to enable customers to valuate their heavy equipment fleet at auction. Here python is used as a medium to apply supervised and unsupervised machine learning techniques to explain 88.90% of the variance observed in the training set and score an RMSLE of 0.745 when predicting values on the test set. In this competition 590 data scientists created predictive models based on a 'training dataset', provided by Fast Iron, and then used those models to predict sale prices on a 'test set' to compete for a $10,000 dollar award for the team or individual with the most accurate model. The model and methods used for my entry, which scored in the upper 20%, is shown in BlueBook.ipynb.

caravel-airbnb

clean_pandas

Demonstration of clean dataframe analysis using Pandas library

collie

A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.

coursera-data-science-johns-hopkins

repo for coursera data science course by johns hopkins

courses

fast.ai Courses

cta_eta

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.

fashion_tags

A competition to build fashion image tagging models.

flink

Mirror of Apache Flink

great_outdoors

h2o-3

Fast Scalable Machine Learning API For Smarter Applications (Deep Learning, GBM, GLM...)

jscottcronin.github.io

Build a Jekyll blog in minutes, without touching the command line.

kaggle-rossman

Submission for the Rossman Kaggle Competition

lessons-learned-data-science-interviews

Lessons learned the hard way through over 30+ data science interviews

libfm

Library for factorization machines

libfm_and_pywfm

Learning to use libfm and pywfm on movielens dataset

luigi_as_pipeline

playing around with luigi module

metrics

Machine learning metrics for distributed, scalable PyTorch applications.

noise_reduction

odsc_west_2019_visual_search

Lessons learned deploying a deep learning visual search service at scale

pinkslipper