lovewenlee Goto Github PK

aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

ad-papers

Papers on Computational Advertising

advanced-machine-learning-with-python

Code repository for Advanced Machine Learning with Python, published by Packt

colornet

Neural Network to colorize grayscale images

darkflow

translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++

data-algorithms-book

MapReduce, Spark, Java, and Scala for Data Algorithms Book

deeplearningflappybird

Flappy Bird hack using Deep Reinforcement Learning (Deep Q-learning).

drl-flappybird

Playing Flappy Bird Using Deep Reinforcement Learning (Based on Deep Q Learning DQN using Tensorflow)

fasttext

Library for fast text representation and classification.

html

hubot

A customizable life embetterment robot.

intelligentweb

This is a clone of the code of the Yooreka project (code used in the book Algorithms of the Intelligent Web, Manning - http://www.manning.com/marmanis/ ) with minor modifications to make it easier to setup on non-Windows platforms.

jalpc_jekyll_theme

🍎Jalpc -- Jekyll Theme

lovewenlee.github.io

machine_learning

mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

papers-we-love

Papers from the computer science community to read and discuss.

predictiveanalatics-using-dl4j-and-apachespark

Predictive analatics using deepLearning4j and Spark

programming-collective-intelligence-code

Examples from Programming Collective Intelligence

scikit-learn

scikit-learn: machine learning in Python

spark

Mirror of Apache Spark

spark-timeseries

A library for financial and time series calculations on Apache Spark

tesseract

Tesseract Open Source OCR Engine (main repository)

test

thunder

scalable analysis of images and time series

wormhole

yellowbrick

A suite of visual analysis and diagnostic tools to facilitate feature selection, model selection, and parameter tuning for machine learning.