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Vincent Granville Male Issaquah, WA United States: Like

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Blog Posts (763)
Discussions (2905)

Vincent Granville's Discussions

Outliers in Logistic Regression

Started Apr 4, 2019 0 Replies 0 Likes

Powerful, Hybrid Machine Learning Algorithm with Excel Implementation

Started Jun 12, 2018 0 Replies 0 Likes

Question about Some Statistical Distributions (Updated)

Started Feb 12, 2018 0 Replies 0 Likes

Generalized Coefficient of Correlation for Non-Linear Relationships

Started this discussion. Last reply by Timothy Gardner Feb 17, 2018. 2 Replies 2 Likes

Two Great Courses on Deep Learning and AI

Started Aug 10, 2017 0 Replies 0 Likes

Hiking Challenge

Started this discussion. Last reply by James Hillstrand Feb 27, 2017. 5 Replies 0 Likes

Challenge: Representation of Numbers as Infinite Products

Started Jan 4, 2017 0 Replies 0 Likes

Difference between Machine Learning, Data Science, AI, Deep Learning, and Statistics

Started Jan 3, 2017 0 Replies 0 Likes

Your Guide to Master Hypothesis Testing in Statistics

Started Jan 2, 2017 0 Replies 0 Likes

R moves up to 5th place in IEEE language rankings

Started Jan 2, 2017 0 Replies 0 Likes

Great Saturday Reading - Best of the Week

Started Dec 17, 2016 0 Replies 0 Likes

Deep Learning in Python: Getting Started

Started Dec 10, 2016 0 Replies 0 Likes

Great Weekend Reading

Started Nov 12, 2016 0 Replies 0 Likes

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Vincent Granville posted a blog post

Quick Primer On Graph Data Structure

While many of the programming libraries encapsulate the inner working details of graph and other algorithms, as a data scientist it helps a lot having a reasonably good familiarity of such details. A solid understanding of the intuition behind such algorithms not only helps in appreciating the logic behind them but also helps in making conscious decisions about their applicability in real life cases. There are several graph based algorithms and most notable are the shortest path algorithms. …See More

Tuesday

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Vincent Granville posted a blog post

TensorFlow 1.x vs 2.x. – summary of changes

In 2019, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators for scaled serving, and the two APIs are…See More

Jan 9

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Vincent Granville posted a blog post

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 overused trope since “once upon a time”. Seriously, just how many…See More

Jan 7

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Vincent Granville posted blog posts

Dec 17, 2019

Profile Information

Short Bio:: Well rounded, visionary data science executive with broad spectrum of domain expertise, technical knowledge, and proven success in bringing measurable added value to companies ranging from startups to fortune 100, across multiple industries (finance, Internet, media, IT, security) and domains (data science, operations research, machine learning, computer science, business intelligence, statistics, applied mathematics, growth hacking, IoT).

Vincent developed and deployed new techniques such as hidden decision trees (for scoring and fraud detection), automated tagging, indexing and clustering of large document repositories, black-box, scalable, simple, noise-resistant regression known as the Jackknife Regression (fit for black-box, real-time or automated data processing), model-free confidence intervals, bucketisation, combinatorial feature selection algorithms, detecting causation not correlations, and generally speaking, the invention of a set of consistent robust statistical / machine learning techniques that can be understood, implemented, interpreted, leveraged and fine-tuned by the non-expert. Vincent also invented many synthetic metrics (for instance, predictive power and L1 goodness-of-fit) that work better than old-fashioned stats, especially on badly-behaved sparse big data. Some of these techniques have been implemented in a Map-Reduce Hadoop-like environment. Some are concerned with identifying true signal in an ocean of noisy data.

Vincent is a former post-doctorate of Cambridge University and the National Institute of Statistical Sciences. He was among the finalists at the Wharton School Business Plan Competition and at the Belgian Mathematical Olympiads. Vincent has published 40 papers in statistical journals (including Journal of Number Theory, IEEE Pattern analysis and Machine Intelligence, Journal of the Royal Statistical Society, Series B), a Wiley book on data science, and is an invited speaker at international conferences. Vincent also created the first IoT platform to automate growth and content generation for digital publishers, using a system of API's for machine-to-machine communications, involving Hootsuite, Twitter, and Google Analytics.

Vincent's profile is accessible at http://bit.ly/1jWEfMP and includes top publications, presentations, and work experience with Visa, Microsoft, eBay, NBC, Wells Fargo, and other organisations.

My Website or LinkedIn Profile (URL):: http://www.linkedin.com/in/vincentg

Field of Expertise:: Data Mining, Marketing Databases, Web Analytics, Statistical Consulting, Other

Years of Experience in Analytical Role:: 15+

Professional Status:: C-Level

Interests:: Networking, New Venture, Other

What is your Favorite Data Mining or Analytical Website?: http://www.analyticbridge.com

What Other Analytical Website do you Recommend?: http://www.datasciencecentral.com

Your Company:: Data Shaping Solutions

Industry:: Internet

How did you find out about AnalyticBridge?: Founder

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Vincent Granville's Blog

Quick Primer On Graph Data Structure

Posted on January 21, 2020 at 10:12am 0 Comments 0 Likes

Continue

TensorFlow 1.x vs 2.x. – summary of changes

Posted on January 9, 2020 at 9:49am 0 Comments 0 Likes

In 2019, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:

Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators…

Continue

The Next Big Thing in AI/ML is…

Posted on January 7, 2020 at 7:41am 0 Comments 0 Likes

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…

Continue

How exactly do you determine causation?

Posted on December 17, 2019 at 2:30pm 0 Comments 0 Likes

Another good article by Ajit Joakar.

Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age

That’s fine. But very few talk of the follow-on question ..

How exactly do you determine causation?

This problem is further compounded because most books and examples are based on standard datasets (ex: Boston, Iris etc) . These examples do not discuss…

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Rule of thumb: Which AI / ML algorithms to apply

Posted on December 17, 2019 at 9:00am 0 Comments 0 Likes

Written by Ajit Jaokar.

Firstly, there are three broad categories of algorithms:

Supervised learning: You know how to classify the input data and the type of behavior you want to predict, but you need the algorithm to calculate it for you on new data
Unsupervised learning: You do not know how to classify the data, and you want the algorithm to find patterns and classify the data for…

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Comment Wall (79 comments)

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At 6:48pm on December 13, 2018, Victor Zurkowski said…: Dear Vincent,

I want to let you know that appreciate your posts. Your excursions in data science, number theory, probabilities, experimental mathematics, etc. are thoughtful, provocative, on occasions infuriating opinionated, and never dull. I also admire how prolific you are.

Here's to many more blog entries,

Victor

At 1:15am on June 24, 2013, Hina Ranglani said…: sir,looking for a answer how
scaling up for high dimensional data& high speed data streams..

At 4:07am on April 12, 2013, Nene Lawani said…: Thanks Vincent! Sorry I missed this earlier...

At 4:06pm on March 10, 2013, Serge Kozlov said…: Thanks for the invite!

At 10:52am on March 10, 2013, Robert Downing said…: Thanks for the invite!

At 3:41am on March 9, 2013, Shane Campbell said…: vincent,

thanks for adding me. can i ask you why you choose to add people like me? didn't see it coming?

regards,

shane

At 6:37am on February 21, 2013, hunter.robertallan said…: Thanks also for the invite. I do have a question about data mining methods and tools. I have a specific situation in mind at work, and would like some professional feedback. Anyone willing to lend an ear and opinion..? thanks...rob

At 4:01am on February 17, 2013, Rishi Jain said…: Hey Vincent,

Sometime back I saw internship material on your site. I am working in the Analytics Industry and looking for improving my technical skills. Please let me know in case you guys are offering any internships which I can pursue along with my job.

Regards,

Rishi

At 3:04pm on December 19, 2012, Vickie Comrie said…: Thanks Vincent! I am honored to be your friend.

Cheers,

Vickie

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