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Vincent Granville's Blog – July 2018 Archive (18)

AutoEncoders with Non-Linear Parameters — KernelML

By Rohan Kotwani.

KerneML

KernelML is brute force optimizer that can be used to train machine learning models. The package uses a combination of a machine learning and monte carlo simulations to optimize a parameter vector with a…

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Added by Vincent Granville on July 29, 2018 at 5:30am — No Comments

Weekly Digest, July 30

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.

Featured Resources and Technical Contributions

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Added by Vincent Granville on July 29, 2018 at 3:30am — No Comments

Don’t Let Data Science Become a Scam

Guest blog by Seth Dobrin and Daniel Hernandez.

Companies have been sold on the alchemy of data science. They have been promised transformative results. They modeled their expectations after their favorite digital-born companies. They have piled a ton of…

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Added by Vincent Granville on July 28, 2018 at 6:30am — 14 Comments

5 major sensor data analytics challenges: deadly or curable?

Guest blog post by Imranali.

A smoothly running sensor data analytics tool may be just as difficult to manage as a symphony orchestra. Because every musician in an orchestra – and every part of an IoT system – needs to work properly and ‘harmonize’ with the others. But how do conductors make their orchestras work so nicely and sound so heavenly instead of creating a mismanaged…

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Added by Vincent Granville on July 27, 2018 at 10:07am — No Comments

Thursday News: AI, Power BI, Neural nets, DL and Object Classification, R, Spark...

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

Resources

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Added by Vincent Granville on July 26, 2018 at 10:32am — No Comments

Thursday News: AI, Power BI, Neural nets, DL and Object Classification, R, Spark...

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

Resources

Continue

Added by Vincent Granville on July 26, 2018 at 10:30am — No Comments

Neural Networks gone wild! They can sample from discrete distributions now!

Guest blog by Yoel Zeldes.

This post describes:

  • what the Gumbel distribution is
  • how it is used for sampling from a discrete distribution
  • how the weights that affect the distribution's parameters can be trained
  • how to use all of that in a toy example (with code)

In this post you will learn what the Gumbel-softmax trick is. Using this trick, you can sample from a discrete…

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Added by Vincent Granville on July 26, 2018 at 10:00am — No Comments

Data Scientist's Book of Quotes

Insights and Advice from Data Science Leaders and Key Influencers. Paperback – July 13, 2018. By Matt Corey.

The Data Scientist’s Book of Quotes includes over 300 insightful and inspiring quotes from the world’s leading Data Science thought leaders and key influencers across the world, including Andrew Ng, Bernard Marr, Vincent Granville, Carla Gentry, Cathy O’Neil and Hilary Mason. The Data Scientist role is one of the most pivotal and disruptive roles in today’s global…

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Added by Vincent Granville on July 26, 2018 at 7:30am — 1 Comment

Bill Vorhies Retrospective: Part 6

Bill is the Editorial Director for Data Science Central, and President and Chief Data Scientist at Data-Magnum, providing predictive analytics and big data infrastructure projects as a service. Bill has been an active commercial predictive modeler since 2001.

In this series consisting  of six…

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Added by Vincent Granville on July 24, 2018 at 7:00pm — No Comments

Weekly Digest, July 23

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.

Featured Resources and Technical Contributions

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Added by Vincent Granville on July 21, 2018 at 7:30am — No Comments

Feature Selection For Unsupervised Learning

This is my presentation for the IBM data science day, July 24.

Abstract

After reviewing popular techniques used in supervised, unsupervised and semi-supervised machine learning, we focus on feature selection methods in these different contexts, especially the metrics used to assess the value of a feature or set of features, be it binary, continuous or categorical…

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Added by Vincent Granville on July 20, 2018 at 9:34am — 2 Comments

Thursday News: Blockchain, AI, NLP, Python, R, SQL, Spark, Regression...

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

Resources

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Added by Vincent Granville on July 19, 2018 at 7:30am — No Comments

Weekly Digest, July 16

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.

Featured Resources and Technical Contributions

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Added by Vincent Granville on July 15, 2018 at 9:30am — 1 Comment

Thursday News: NLP, AI, Deep Learning, Sensor Data, Death of the Data Scientist, DataViz

Here is our selection of articles and technical contributions featured since Monday:

Technical Contributions

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Added by Vincent Granville on July 12, 2018 at 9:00am — No Comments

Weekly Digest, July 9

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.

Featured Resources and Technical Contributions

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Added by Vincent Granville on July 7, 2018 at 7:00am — No Comments

Data Science and Machine Learning: Great List of Resources

All you need to know about machine learning, accessible using our data science search engine, covering hundreds of articles and tutorials:

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Added by Vincent Granville on July 5, 2018 at 7:30am — No Comments

Thursday News: AI, Google, Kaggle, R, New Book, Data Quality, ML

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

Forum Questions and Technical Contributions

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Added by Vincent Granville on July 5, 2018 at 6:30am — No Comments

Is it still possible today to become a self-taught data scientist?

If you are an engineer working for a company like Boeing, have processed and leveraged data extensively over years of professional experience, used data science tools and programming languages, and have success stories, you are de facto a data scientist even if you think you are not, in this case an industrial data scientist, as opposed for instance, to a marketing data scientist. And you are hireable as a data scientist.

If you have a PhD in history, has little experience…

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Added by Vincent Granville on July 3, 2018 at 9:00am — 4 Comments

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