kmclaugh

import pandas as pd
import numpy as np
import seaborn as sns

from keras.layers import Dense
from keras.models import Model, Sequential
from keras import initializers

## ---------- Create our linear dataset ---------------
## Set the mean, standard deviation, and size of the dataset, respectively

kangeugine

states = ('Rainy', 'Sunny')
 
observations = ('walk', 'shop', 'clean')
 
start_probability = {'Rainy': 0.6, 'Sunny': 0.4}
 
transition_probability = {
   'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},
   'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},
   }

ajmaradiaga

import numpy as np

def gradient_descent_runner(points, starting_m, starting_b, learning_rate, num_iterations):
	m = starting_m
	b = starting_b
	
	for i in range(num_iterations):
		m, b = step_gradient(m, b, np.array(points), learning_rate)

	return m, b

pratos

Install Zeppelin in Ubuntu systems

• First install Java, Scala and Spark in Ubuntu

  • Install Java

    sudo apt-add-repository ppa:webupd8team/java
    sudo apt-get update
    sudo apt-get install oracle-java8-installer
    

vpekar

#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Selecting dimensionality reduction with Pipeline and GridSearchCV
=================================================================

This example constructs a pipeline that does dimensionality
reduction followed by prediction with a support vector
classifier. It demonstrates the use of GridSearchCV and

nitish11

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

doowon

#! /bin/sh
### BEGIN INIT INFO
# Provides:          jupyter
# Required-Start:    $remote_fs $syslog
# Required-Stop:     $remote_fs $syslog
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: Start jupyter
# Description:       This file should be used to construct scripts to be
#                    placed in /etc/init.d.

tnachen

# Prepare Steps:
# We are assuming we’re using CUDA 7.5
# Clone and Build latest Mesos with GPUs (skipping instructions here…)
# CUDA prepare script for ubuntu 14.04 with AWS G2 instance. 
# http://tleyden.github.io/blog/2015/11/22/cuda-7-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/
# Start mesos master and slave
# ./mesos-master --work_dir=/tmp/mesos
# sudo GLOG_v=1 ./mesos-slave --isolation=gpu/nvidia --master=`hostname -i`:5050 --nvidia_gpu_devices=0

# Add cuda bin to PATH