Deep Learning For Sequential Data – Part V: Handling Long Term Temporal Dependencies

PERPETUAL ENIGMA

1 mainIn the previous blog post, we learnt why we cannot use regular backpropagation to train a Recurrent Neural Network (RNN). We discussed how we can use backpropagation through time to train an RNN. The next step is to understand how exactly the RNN can be trained. Does the unrolling strategy work in practice? If we can just unroll an RNN and make it into a feedforward neural network, then what’s so special about the RNN in the first place? Let’s see how we tackle these issues.  

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Measuring The Memory Of Time Series Data

PERPETUAL ENIGMA

1-mainTime series data has memory. It remembers what happened in the past and avenge any wrongdoings! Can you believe it? Okay the avenging part may not be true, but it definitely remembers the past. The “memory” refers to how strongly the past can influence the future in a given time series variable. If it has a strong memory, then we know that analyzing the past would be really useful to us because it can tell us what’s going to happen in the future. If you need a quick refresher, you can check out my blog post where I talked about memory in time series data. We have a high level understanding of how we can classify time series data into short memory and long memory, but how do we actually measure the memory?  

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It’s been a hard day’s night of Deep Learning

GetToCode

test_orange_2016

Turning the page

The new year  is round the corner and so are the thoughts about a quest into the hidden layers of Deep Learning.  This year’s goal was to become a developer and it was achieved as planned on time. The main projects were in Android and the end of the year was under the sign of Machine Learning and ,more precisely speaking, Deep Learning.

Summary 

So what are the main points in almost a two month headlong journey on the Deep Learning highway?

Deep Learning Book

  • As you should have already known by now Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville was published. This detailed and helpful book on foundations of artificial neural networks is pretty expensive but can be accessed electronically in html format for free.

Jason Brownlee’s Machine Learning Mastery site comes in handy

  • As for me I started to be interested in the…

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CCIE Program Refresh, My thoughts.

UCSguru.com

The world of technology as we know is changing fast, faster than many predicted. This is certainly true in the data center. In the “Old days” (more than 3 years ago) most of my time was spent evangelising a particular product or adjudicating a bake off between two or more vendor platforms.

These days the infrastructure conversations tend to be far shorter, the fact is infrastructure these days is a given, and the true differentiator is how easy that infrastructure is to consume, automate and orchestrate in a cloud stack or converged solution.

Gone are the days of product led engagements (and rightfully so) these days it’s all about solution led engagements. Taking a business requirement and translating that into a technical solution which truly drives business outcomes.

This solutions led approach, inevitably leads to a closer collaboration between teams across all elements of the cloud stack, portal developers, applications…

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Azure Resource Manager REST calls from Python

MSFT Stack

This article describes how to make REST calls to Azure Resource Manager (ARM) from Python. In particular, how to authenticate. Once you have an authentication token you just add it to your REST call headers when calling the Azure REST API.

Note: If you’re looking for the official Azure SDK for Python, go here: https://github.com/Azure/azure-sdk-for-python.

Initial Setup

Creating an Azure Resource Manager app requires some one-time setup steps:

  • Create an Azure Active Directory App
  • Create a Service Principal (an Active Directory “user” which represents an automated application) and grant it permissions
  • Create a credential  object and get the tenant ID.

These steps are well documented here: Authenticating a Service Principal with Azure Resource Manager, and are covered (using PowerShell) in steps 1-4 of my C# Azure REST write-up here: How to call the Azure Resource Manager REST API from C#.

If you follow these steps you will have the…

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Docker macvlan and ipvlan network plugins

Sreenivas Makam's Blog

This is a continuation of my previous blog on macvlan and ipvlan Linux network drivers. Docker has added support for macvlan and ipvlan drivers and its currently in experimental mode as of Docker release 1.11.

Example used in this blog

In this example, we will use Docker macvlan and ipvlan network plugins for Container communication across hosts. To illustrate macvlan and ipvlan concepts and usage, I have created the following example.

vlan4

Following are details of the setup:

  • First, we need to create two Docker hosts with experimental Docker installed. The experimental Docker has support for macvlan and ipvlan. To create experimental boot2docker image, please use the procedure here.
  • It is needed to enable promiscuous mode on the Virtualbox adapter. This allows for Container communication across hosts.
  • There are four Containers in each host. Two Containers are in vlan70 network and two other Containers are in vlan80 network.
  • We will…

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Email spam detection using apache spark mllib

Knoldus

In this blog we will see the real use case of spark mllib that is email spam detection. With the help of using the apache spark mllib component we will detect that email will goes in spam folder or primary folder.

So now jump into the programming and see how it will implement. So first we will load the data from training from spam dataset and primary dataset as follow

val spam = sc.textFile("/home/sandy/Spark/enron1/spam/0052.2003-12-20.GP.spam.txt", 4)
val normal = sc.textFile("/home/sandy/Spark/enron1/ham/0022.1999-12-16.farmer.ham.txt", 4)

Next we need to use HashinTF or IDF to find the frequency of word in the mail and create a Vector which is helpful in creating the LabelPoints for the training

val spamFeatures = spam.map(email => tf.transform(email.split(" ")))
val normalFeatures = normal.map(email => tf.transform(email.split(" ")))

With the help of vectors we will create the LabelPoints , LabelPoints are the input for our model we will create label points as follows

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