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October 2018 Blog Posts (89)

Deep Learning Networks: Advantages of ReLU over Sigmoid Function

This was posted as a question on StackExchange. The state of the art of non-linearity is to use rectified linear units (ReLU) instead of sigmoid function in deep neural network. What are the advantages? I know that training a network when ReLU is used would be faster, and it is more biological inspired, what are the other advantages? (That is, any disadvantages of using sigmoid)?…

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Added by L.V. on October 11, 2018 at 6:00pm — No Comments

Thursday News: AI, Python, R, Excel, BI, Reinforcement Learning, NLP, 'No Code' Data Science

This is our selection of featured resources and articles posted since Monday.

Resources (Technical)

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Added by Vincent Granville on October 11, 2018 at 8:00am — No Comments

Are you buying an apartment? How to hack competition in the real estate market with data monitoring



In the last couple of years, real estate companies have shifted their focus to the digital world, and now almost all investments have an online system showing what apartments are available. This is very convenient for their potential clients, as they can easily become familiar with the apartments on offer. Things become interesting when all available data is monitored on a weekly basis, and sales progress is analysed.

Why is this so important? As sales…
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Added by Michał Frącek on October 11, 2018 at 1:30am — No Comments

Mixing & Matching in R for Data Science

I've spent time over the last few months attempting to enhance my skills in the statistical sub-field of causal inference.

Overly simplified, causal inference comprises a series of methodologies and techniques to assist analysts in making the jump from association or correlation to cause and effect. How can one progress from noting a correlation between…

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Added by steve miller on October 10, 2018 at 10:00am — 5 Comments

Building Shiny Apps: Tutorial

This article was posted by Dean Attali. Dean is currently an R-Shiny consultant.

Shiny is a package from RStudio that can be used to build interactive web pages with R. While that may sound scary because of the words “web pages”, it’s geared to R users who have 0 experience with web development, and you do not need to know any HTML/CSS/JavaScript.

You can do quite a lot…

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Added by Emmanuelle Rieuf on October 10, 2018 at 9:30am — No Comments

Reinforcement Learning: Super Mario, AlphaGo and beyond

Most of the literature we find on machine learning talks about two types of learning techniques – supervised and unsupervised. Supervised learning is where we have a labelled dataset. This means we already have data from which to develop models using algorithms such as Linear Regression, Logistic Regression, and others. With this model, we can make further predictions like given data on housing prices, what will the cost of a house with a given set of features…

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Added by Divya Singh on October 10, 2018 at 7:37am — No Comments

Free eBook: Enterprise AI - An Applications Perspective

By Ajit Jaokar and Cheuk Ting Ho.

Exclusively for Data Science Central members, with free access. You can download this book (PDF) here

Introduction

Enterprise AI: An applications perspective takes a use case driven approach to understanding the deployment of AI in the Enterprise. Designed for strategists and…

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Added by Vincent Granville on October 10, 2018 at 6:00am — 4 Comments

88 Resources & Tools to Become a Data Scientist

Harvard Business Review has regareded data scientist as the sexiest job of the 21st century. In this article, with the assistance of Octoparse V7, we aggregated the resources and tools that you may need to become a data scientist.

1.…

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Added by Paul Black on October 9, 2018 at 5:30pm — 1 Comment

Deep Learning Research Review: Natural Language Processing

This article was written by Adit Deshpande on his own blog.

This is the 3rd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on applying deep learning to Natural Language Processing. The …

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Added by Emmanuelle Rieuf on October 9, 2018 at 3:30pm — No Comments

Practicing ‘No Code’ Data Science

Summary:  We are entering a new phase in the practice of data science, the ‘Code-Free’ era.  Like all major changes this one has not sprung fully grown but the movement is now large enough that its momentum is clear.  Here’s what you need to know.

 

We are entering a new phase in the practice of data science, the ‘Code-Free’…

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Added by William Vorhies on October 9, 2018 at 9:54am — 3 Comments

Why Excel Users Should Learn Python

Latest update: November 16, 2018

Microsoft Excel has been around for over 30 years now, and chances are it's not going to change in the foreseeable future. In fact, Excel is facing immense competition from challengers such as Google Spreadsheets and well-funded start-ups like Airtable, which are both going after Excel's massive user base of approximately 500 million worldwide. Tech-savvy small and mid-sized businesses embrace innovative alternatives to Excel. However,…

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Added by Rafael Knuth on October 9, 2018 at 7:30am — 2 Comments

Supervised Learning – Everything You Need To Know

Supervised learning – A blessing we have in this machines era. It helps to depict inputs to outputs. It uses labelled training data to deduce a function which has set of training examples. The majority of practical machine learning uses supervised learning as on date.

What is Machine Learning

AILabPage defines Machine Learning  as A focal point where business, data and experience meets…

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Added by Vinod Sharma on October 8, 2018 at 10:00am — No Comments

Pattern Recognition Objects and the Internal Conception of Meaning

A candlestick chart is sometimes used by stock market technicians to make trading decisions.  Each candlestick graphically depicts the following prices simultaneously:  opening, closing, high, and the low.  An interesting question is whether candlesticks can be used for pattern recognition purposes.  Traders directly interpret the charts - applying a type of pattern recognition that might involve industry norms and personal judgment.  But I mean using a systematic or algorithmic…

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Added by Don Philip Faithful on October 8, 2018 at 5:46am — No Comments

Weekly Digest, October 8

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.

Announcement

  • Big data is a big buzzword in the business world. But it’s a serious concept, referring to data sets so large and complex that traditional data-processing applications simply…
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Added by Vincent Granville on October 7, 2018 at 8:30am — No Comments

Decision Tree Classifier Implementation In R

This article was written by 

The decision tree classifier is a supervised learning algorithm which can use for both the classification and regression tasks. As we have explained the building blocks of …

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Added by Emmanuelle Rieuf on October 6, 2018 at 11:30am — No Comments

Something for nothing: the shortcomings of self-serve BI

Recently, I read an article from CIO.com examining the 5 pitfalls of self-service BI. The author, Thor Olavsrud did a fantastic job explaining how just having a tool doesn’t mean that people will use it properly. “Garbage-in, garbage-out” is a phrase I’ve…

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Added by Matthew Gierc on October 5, 2018 at 7:39pm — 1 Comment

20 Great Articles about AI

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, …

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Added by Andrei Macsin on October 5, 2018 at 10:00am — No Comments

AI responsibility: Taming the algorithm

We've reached a point where human (cognitive) task performance is being leveraged or even replaced by AI. So who or what is responsible for what this AI does?

While the question seems simple enough, legal answers from the field are apparently opaque and embroiled. This is caused by the fact that AI is performing human-like tasks without having the clear legal accountability of one, and the question is whether it should have any. Fortunately, now that machine learning and artificial…

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Added by Roger van Daalen on October 5, 2018 at 5:21am — No Comments

Ajit Jaokar Retrospective: AI, ML, IoT, Data Science Articles

Ajit's research is focused on Data Science for IoT. He teaches the same at Oxford University and UPM in Madrid (@forumoxford + @citysciences). Ajit is also launching a course / certification in Data Sciences for Industrial IoT. His personal research interests include Deep Learning algorithms for IoT/future city domains.

The selection below…

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Added by Vincent Granville on October 4, 2018 at 9:00am — No Comments

Thursday News: AI, IoT, Datasets, Deep Learning, Python vs R

This is our selection of featured articles and resources posted since Monday:

Technical Resources

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Added by Vincent Granville on October 4, 2018 at 8:00am — No Comments

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