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Influencer is the one who can make an impact towards a service/product by giving his opinion towards it. There by he can influence his friends, peers or his followers in particular platform such as twitter, facebook, instagram etc. He can also associate with popular brands across the globe and promote their products.

So what about predicting the influence of any user for a particular platform? Interesting.. right?

Yeah, In this blog post we will build such a model and predict the influencer score of those users.

Let’s do it for twitter platform.


In this article we are going to learn how we can create a simple dropwizard application in java and dockerize the application, finally we’ll deploy this containerized application on to kubernetes.

Prerequisites

So, to start with, we must first ensure that we have installed the following

$brew cask install docker

In this blog post we’ll be creating a playing card detector — finding out which cards are present in the image(hearts of king, clubs of three etc). We will be using a pre-trained classifier with specific neural network architectures .

Before dive deep let me first briefly explain object detection and classification.

Detection Vs Classification

Fig 1.

When performing image classification, we present one input image to the network and obtain one class label out.(Fig 1)

Fig 1 will be recognised as King of hearts(Kd) by the system.


Services → Lambda → Functions → Create function

Choose create custom rule → Give role name → Edit Policy document as below

{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "arn:aws:logs:*:*:*"
},
{
"Effect": "Allow",
"Action": [
"ec2:Start*",
"ec2:Stop*"
],
"Resource": "*"
}
]
}

Click Allow → Give lambda function name ‘ec2start’ → Next

Put below code to the body of lambda function

import boto3
region = '' ## region, eg: us-east-1
instances = [''] ##instance ids
def lambda_handler(event, context):
ec2 = boto3.client('ec2', region_name=region)
ec2.start_instances(InstanceIds=instances)
print 'started your instances: ' + str(instances)


How about building your own assistant which will reply for the conversations it is trained for. Interesting?

Okay, let’s learn to build.

So In this blogpost, We’ll build a conversational bot on Rasa stack which will interact with you and it can provide random jokes if you ask.

Requirements

Rasa Stack : It is a set of open source machine learning tools for developers to create contextual AI assistants and chatbots and the leading open source machine learning toolkit that allow developers expand bots beyond answering simple questions with minimal training data. It has two frameworks, NLU & Core.

Fig 1. rasa-stack

Rasa…

Akhil C K

Programmer

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