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Understanding the applications of Probability in Machine Learning

 

This post is part of my forthcoming book The Mathematical Foundations of Data Science. Probability is one of the foundations of machine learning (along with linear algebra and optimization). In this post, we discuss the areas where probability theory could apply in machine learning applications. If you want to know more about…

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Added by ajit jaokar on October 27, 2019 at 10:30am — No Comments

Weekly Digest, October 28

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  

Featured Resources and Technical…

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Added by Vincent Granville on October 27, 2019 at 9:00am — No Comments

Jupyter Notebooks: Fundamentals of Machine Learning and Deep Learning

Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. 

Source: from the Support Vector Machines chapter,…

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Added by Capri Granville on October 27, 2019 at 6:30am — No Comments

Machine Learning and Deep Learning Textbook - Cornell University

Another free book to learn Machine Learning. It also comes with a Youtube video series available here

Content

  • Machine Learning Setup
  • k-Nearest Neighbors / Curse of…
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Added by Capri Granville on October 27, 2019 at 6:30am — No Comments

Fun Math: Infinite Nested Radicals of Random Variables - Connection with Fractals and Brownian Motions

Some original and very interesting material is presented here, with possible applications in Fintech. No need for a PhD in math to understand this article: I tried to make the presentation as simple as possible, focusing on high-level results rather than technicalities. Yet, professional statisticians and mathematicians, even academic researchers, will find some deep and fascinating results worth further exploring. Source code and Excel spreadsheets are provided for replication…

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Added by Vincent Granville on October 26, 2019 at 12:00pm — 2 Comments

Comparing Data Sets in One Picture

Which statistical method you use to compare data sets depends on two main factors: your overall goal and the type of data you have. Parametric data means that you know the underlying distribution (for example, your data might be normally distributed).…

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Added by Stephanie Glen on October 26, 2019 at 8:45am — No Comments

Digital Transformation Horizon 1 to Horizon 3:  When Is Innovation Really Innovation?

In the early 2000’s, IBM Deep Blue was on the lookout for its next Grand Challenge.  It had achieved an exhilarating win over chess grand master Garry Kasparov in 1997.  But that was a game with deterministic outcomes where superior processing power gave Deep Blue a significant advantage over even a human grand champion.  IBM needed a challenge commiserate with its Artificial Intelligence aspirations, and the wildly popular TV game show…

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Added by Bill Schmarzo on October 26, 2019 at 2:17am — No Comments

How to Become a Data Scientist on your Own

Originally posted by Zeeshan Usmani in May 2015.

Big Data, Data Sciences, and Predictive Analytics are the talk of the town and it doesn’t matter which town you are referring to, it’s everywhere, from the White House hiring DJ…

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Added by Vincent Granville on October 25, 2019 at 6:25am — No Comments

The Role of Big Data in Customer Software Development

The process of software development is rapidly evolving with the emerging new technologies being introduced in the field. In order for any software development service to be reliable and successful, it is necessary for the software development team to keep up with these technologies.

Some of the latest tech trends influencing the field…

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Added by Hardik Shah on October 25, 2019 at 4:44am — No Comments

The Complete Data Science LinkedIn Profile Guide

Why Data Scientists Should Be Using LinkedIn

To date, there are more than 830,000 data science LinkedIn profiles registered worldwideDespite this number of Data Scientists available/in roles online currently, it’s no secret there is still a major talent shortageIn fact,…

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Added by Matt Reaney on October 25, 2019 at 3:30am — No Comments

Higher Education–Universities or MOOCs?

You have probably heard about the concept of massive open online courses (MOOC's) in the last few years. It is an alternative to formal education and is seen by some as the way of the future. Have you…
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Added by Marija Zoldin on October 25, 2019 at 1:30am — No Comments

5G Networks Security Risks

In a modern world of advanced technologies, our life is literally impossible without cellular telecommunications. Today, smartphones and wireless broadband Internet are common things for everybody. And recently, all developments and innovations in cellular technology are not about the availability of mobile connectivity but about bringing it to a…

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Added by David Balaban on October 24, 2019 at 9:32pm — No Comments

Thursday News, October 24

Here is our list of featured articles and technical resources posted since Monday:

Announcement:

  • Scaled for your Workload. Configure your Machine Learning Server. GPU-accelerated power and performance to deliver the most efficient, comprehensive solution, from processing massive datasets for scientific visualization and simulation to final batch rendering in the data…
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Added by Vincent Granville on October 24, 2019 at 11:30am — No Comments

How to make you own Wiki from Wikipedia using Python

Here is a short blog I was asked to make about making a personal Wiki from Wikipedia. It shows the basic steps in text processing so I hope it will be useful for data scientists. It also requires some knowledge of MediaWiki setup on a web server, and some (not very advanced) knowledge of the Python programming language. It takes only several days to create this Wiki with Wikipedia articles if you know…

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Added by jwork.ORG on October 24, 2019 at 1:21am — No Comments

Why You Should Integrate Big Data into Your .NET solutions

As the world continues to collect more and more data with every passing second, it is only understandable that we would need tools that would help us make sense of all the data at our disposal. There are undoubtedly a variety of potent tools that are capable of doing just that; big data has managed to stand out from the crowd. Why? Primarily because it brings with it the promise of just the kind of insights and intelligence businesses need to not only improve their ability to gauge precisely…

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Added by Ryan Williamson on October 23, 2019 at 10:02pm — No Comments

Factors that contributed to the economic crisis in the USA

Problem detected

What factors contributed to the economic crisis in the United States of America?

The question above is actually also being asked by the people there (USA), and even the world community. With the value of each proportion (in percentage).

There are several things that need to be underlined, including:

  1. it is known, that financial factors have contributed 18% or the largest of the 7 factors + others.
  2. but, when viewed from consumer…
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Added by Jeefri A. Moka on October 23, 2019 at 9:01am — No Comments

What is Cybersecurity Data Science?

Cybersecurity Data Science (CSDS) is a rapidly emerging profession focused on applying data science to prevent, detect, and remediate expanding and evolving cybersecurity threats. CSDS is increasingly formally recognized as a cybersecurity job specialty, for instance in the NIST NICE Cybersecurity Workforce Framework.

A proposed CSDS definition derived from…

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Added by Scott Mongeau on October 22, 2019 at 11:20pm — No Comments

Hands-on python: my preamble

I prefer to structure my code the same way as an article, and if have academic background as well, you can relate. Hence, I usually start with preamble, where I put all the packages and toolkits I would like to use. Then, the main part follows and subsequently the rest (for example, where the result of the model should go, all the connections). Especially if you are getting started and learn python, I recommend to structure and comment your code as clear as possible. With Jupiter you have…

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Added by Dr. Katharina Glass on October 22, 2019 at 8:30pm — No Comments

Dare to start simple

Imagine, you are the first data scientist in a company, may be in the industrial company, in one of the old industries, old economy branches. Then, you are a unicorn. Basically, you start data science from the scratch: you must introduce, explain, promote and establish data science. To manage this challenging task, dare to simple!

Three years ago, I was exactly there – the first and for some time the only data scientist in the traditional, old industry company. The challenge was to…

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Added by Dr. Katharina Glass on October 21, 2019 at 9:21pm — No Comments

The Next Big Thing in AI/ML is…

Summary:  AI/ML itself is the next big thing for many fields if you’re on the outside looking in.  But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.

 

“The Next Big Thing in AI/ML is…” as the lead to an article is probably the most…

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Added by William Vorhies on October 21, 2019 at 9:18am — 1 Comment

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