stanlee321

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
    

stanlee321

from keras.callbacks import Callback
import keras.backend as K
import numpy as np

class SGDRScheduler(Callback):
    '''Cosine annealing learning rate scheduler with periodic restarts.

    # Usage
        ```python
            schedule = SGDRScheduler(min_lr=1e-5,

stanlee321

Synopsis

At the top of the file there should be a short introduction and/ or overview that explains what the project is. This description should match descriptions added for package managers (Gemspec, package.json, etc.)

Code Example

Show what the library does as concisely as possible, developers should be able to figure out how your project solves their problem by looking at the code example. Make sure the API you are showing off is obvious, and that your code is short and concise.

Motivation

stanlee321

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. author2vec http://dl.acm.org/citation.cfm?id=2889382
5. bb2vec https://arxiv.org/pdf/1809.09621.pdf
6. behavior2vec https://dl.acm.org/citation.cfm?id=3184454
7. care2vec https://arxiv.org/abs/1812.00715
8. cat2vec http://104.155.136.4:3000/forum?id=HyNxRZ9xg

stanlee321

version: '2'
services:
    myapp:
        build: .
        container_name: "myapp"
        image: debian/latest
        environment:
            - NODE_ENV=development
            - FOO=bar
        volumes:

stanlee321

 import matplotlib.pyplot as plt

stanlee321

Introduction

This quick guide describes how to create a Jupyter Notebook in AWS EC2 then how to access it remotely using SSH tunneling. This method is preferred since you do not open any additional ports besides 22, requires little-to-no configuration, and is generally more straight-forward.

Pre-requisites

This current version assumes basic familiarity with cloud computing, AWS services, and Jupyter Notebook. Mostly because this version won't have images and won't dive too deep into each individual step.

Steps

Spin-up EC2 instance with "Deep Learning" AMI

1. Log into EC2 console and click "Launch Instance" button.
2. Inside "AWS Marketplace", select the "Deep Learning AMI" from AWS. I use this AMI because most of the stuff you'll need is installed already.

stanlee321

import requests
import json
import cv2

addr = 'http://localhost:5000'
test_url = addr + '/api/test'

# prepare headers for http request
content_type = 'image/jpeg'
headers = {'content-type': content_type}

stanlee321

stanlee321

import numpy as np
from keras import backend as K
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix

#Start
train_data_path = 'F://data//Train'