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Four short links: 18 October 2017

By | ai, bigdata, machinelearning

Bitmarks, Stephen Fry, Continuous Deployment, and Better Web Text Editor

  1. Bunnie Uses BitmarksFor the cost and convenience of a humble printed label, Bitmark enhances control over our factories, enables production lot traceability, deters cloning, prevents warranty fraud, enhances confidence in the secondary market, and gives us ample options to streamline our reverse logistics.
  2. Stephen Fry Lecture (YouTube) — great lecture on technology and morality and our future, and the Q&A is even better!
  3. World-Class Continuous Deployment Using Free Hosted Tools (Simon Willison) — a nice how-to. (And delightful to see Simon blogging again)
  4. ProseMirror 1.0 — a web rich-text editor that is agnostic to the actual document shape, making it possible to build applications on top of this library that in the past would have required a fully custom editor implementation.

Continue reading Four short links: 18 October 2017.




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Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlow

By | iot, machinelearning

Can Machines Really Learn? 

Machine learning (ML) is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. 

Machine learning has become an essential pillar of IT in all aspects, even though it has been hidden in the recent past. We are increasingly being surrounded by several machine learning-based apps across a broad spectrum of industries. From search engines to anti-spam filters to credit card fraud detection systems, list of machine learning applications is ever-expanding in scope and applications. 

The goal of this book is to provide you with a hands-on, project-based overview of machine learning systems and how they are applied over a vast spectrum of applications that underpins AI technology from Absolute Beginners to Experts. 

This book is a fast-paced, thorough introduction to Machine Learning that will have you writing programs, solving problems, and making things that work in no time. 

This book presents algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

  • Supervised and Unsupervised learning methods
  • Artificial Neural Networks
  • Hands-on projects based on Real-world applications
  • Bayesian learning method
  • Reinforcement learning
  • And much more

By the end of this book, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.

Learning Outcomes: By the end of this book, you will be able to:

  • Identify potential applications of machine learning in practice
  • Describe the core differences in analyses enabled by regression, classification, and clustering
  • Select the appropriate machine learning task for a potential application
  • Apply regression, classification, and clustering
  • Represent your data as features to serve as input to machine learning models
  • Utilize a dataset to fit a model to analyze new data
  • Build an end-to-end application that uses machine learning at its core
  • Implement these techniques in Python

If you’ve been thinking seriously about digging into ML, this book will get you up to speed. Why wait any longer?

$9.99



How to manage Docker containers in Kubernetes with Java

By | ai, bigdata, machinelearning

Orchestrate production-ready systems at enterprise scale.

In Containerizing Continuous Delivery in Java we explored the fundamentals of packaging and deploying Java applications within Docker containers. This was only the first step in creating production-ready, container-based systems. Running containers at any real-world scale requires a container orchestration and scheduling platform, and although many exist (i.e., Docker Swarm, Apache Mesos, and AWS ECS), the most popular is Kubernetes. Kubernetes is used in production at many organizations, and is now hosted by the Cloud Native Computing Foundation (CNCF). In this article, we will take the previous simple Java-based, e-commerce shop that we packaged within Docker containers and run this on Kubernetes.

The “Docker Java Shopfront” application

The architecture of the “Docker Java Shopfront” application that we will package into containers and deploy onto Kubernetes can be seen below:

Continue reading How to manage Docker containers in Kubernetes with Java.




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What Are The Potential Dangers Of Quantum Computing?

By | iot


The development of quantum computers may create serious cyber-security threats. The NSA has recently released statements expressing their concern over the potential of quantum computing to foil the cryptography protecting all data to date. Furthermore, the use of quantum computers may become widespread before many people expect.

What Are The Potential Dangers Of Quantum Computing?

A powerful quantum computer could crack the cryptographic algorithms that keep our data safe. While managed detection and response services are highly effective at keeping today’s data safe, these services wouldn’t be able to protect data from a quantum computer. Quantum computers could even break the algorithms that are used by New York’s stock exchange. This could lead to the collapse of the stock market.

How Do Quantum Computers Work?

Quantum computers use quarks as bits (qubits) rather than bits made of silicon. The device’s bits are the smallest particles in the universe, and this will make it possible for quantum computers to have a larger number of bits than conventional devices.

Quantum computers could have drives that contain hundreds of millions of terabytes or more. It’s even possible that mobile devices could be this powerful!

Also, quantum computers use a code that is different than binary code. In fact, quarks …

Read More on Datafloq

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Markets Performance after Election: Day 239

By | ai, bigdata, machinelearning

(This article was first published on R – Quintuitive, and kindly contributed to R-bloggers)

When I wrote the original post, I wasn’t planning on writing a follow-up. Certainly not the week after. But what a difference a week can make in a dynamic system like the US stock market.

While re-running the computations testing the latest version of RStudio, I noticed something surprising – President Trump’s rally has advanced to 2nd place!

A mere week ago, that seemed unthinkable. Something abnormal must have happened. Two things happened. First, the current stock market advanced another 2%. Nothing to brag about just another positive vote of confidence in the economy’s direction.

The more impactful reason behind this sudden switch became clear when I took a look at the President H.W. Bush’s rally. Even from the above chart, it’s clear the rally lost significant amount of steam within a day, or two max. Is this a problem with data? A bit of forensics:

election.date = "1988-11-08"

require(quantmod)
dj = getSymbols("^DJI", from="1900-01-01", auto.assign=F)
id = findInterval(as.Date(election.date), index(dj))
tail(ROC(Cl(dj[id:(id+239)]), type="discrete", na.pad=F))

And the truth reveals itself:

Date Return
1989-10-12 -0.49%
1989-10-13 -6.91%
1989-10-16 3.43%
1989-10-17 -0.70%
1989-10-18 0.19%
1989-10-19 1.50%

Low and behold, no problem with the data, just a 7% drop on October 13th 1989. A quick search reveals that this was indeed Friday the 13th mini-crash! Friday, the 13th … mini-crash … What a coincidence!

I will keep it brief and wrap up this post here. There were a few improvements and changes I did to the R code used to perform these analysis – the Gist contains all of them.

It’s all optimism in the air, judging by the market behavior at least.

The post Markets Performance after Election: Day 239 appeared first on Quintuitive.

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Michael Nygard on architecture without an end state

By | ai, bigdata, machinelearning

The O’Reilly Programming Podcast: Embracing late changes, plurality, and decentralization.

In this episode of the O’Reilly Programming Podcast, I talk with Michael Nygard, a software architect at Cognitect. He has spoken about “architecture without an end state” at numerous O’Reilly Software Architecture events, and he is the author of the book Release It! Design and Deploy Production-Ready Software.

Continue reading Michael Nygard on architecture without an end state.




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