Emmanuelle Rieuf's Blog (204)

How well do facial recognition algorithms cope with a million strangers?

This article was written by Jennifer Langston. Co-authors include UW computer science and engineering professor Steve Seitz, undergraduate student and web developer Evan Brossard and former student Daniel Miller.…


Added by Emmanuelle Rieuf on May 11, 2017 at 6:30am — No Comments

New Trends in Artificial Intelligence & Machine Learning

This article was written by Hardik Gohil, Sr Content Writer. 

Artificial Intelligence has effectively convinced its necessity to the entire world by performing excellently in various industries. Almost all the industries including manufacturing, healthcare, construction, online retail, etc. are adapting to the reality of IoT to leverage its advantages.

Machine learning technology is…


Added by Emmanuelle Rieuf on May 10, 2017 at 11:30am — No Comments

A curated list of resources dedicated to bayesian deep learning

This article comes from GitHub.

A curated list of resources dedicated to bayesian deep learning.


  1. Deep gaussian processes|Andreas C. Damianou,Neil D. Lawrence|2013 -- Source:…

Added by Emmanuelle Rieuf on May 10, 2017 at 7:30am — No Comments

Time Series Analysis With Generalized Additive Models

This article comes from Algobeans Layman tutorials in analytics. 

Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time—we are always interested to foretell the future.

Temporal Dependent Models

One intuitive way to make…


Added by Emmanuelle Rieuf on April 29, 2017 at 3:00pm — No Comments

Machine Learning Skills Among Data Scientists

This article was posted by Bob E. Hayes on Customer think. Bob, PhD is Chief Research Officer at Appuri. He a scientist, blogger and author on CEM and data science.

Data scientists have a variety of different skills that they bring to bear on Big Data projects. These skills cut across Subject Matter Expertise, Technology, Programming, Math & Modeling and Statistics. One valuable…


Added by Emmanuelle Rieuf on April 18, 2017 at 9:00am — No Comments

Ranking All 50 States by Average Credit Score of its Citizens

This article was contributed by Statistical Future.

Whether you want it to or not, credit and its availability plays a major role in everyone’s life, whether or not you directly experience it. For the average person, credit scores are mainly going to be used for three things: buying a house, buying a car, and using credit cards.

In the world of business, things get exponentially more complicated and it also ended up leading to a horrible housing crash and recession…


Added by Emmanuelle Rieuf on April 15, 2017 at 9:30am — 1 Comment

The Startup Founder’s Guide to Analytics

This article was written by Tristan Handy. Tristan is the founder and president of Fishtown Analytics: helping startups implement advanced analytics.

I’m very confident of that, because today, everyone needs analytics. Not just product, not just marketing, not just finance… sales, fulfillment, everyone at a startup needs analytics today.…


Added by Emmanuelle Rieuf on April 10, 2017 at 11:00am — No Comments

Implement an ARIMA model using statsmodels (Python)

In this article was written by Michael Grogan. Michael is a data scientist and statistician, with a profound passion for statistics and programming.

In a previous tutorial, I elaborated on how an ARIMA model can be implemented using R. The model was fitted on a stock price dataset, with a (0,1,0) configuration being used for ARIMA.

Here, I detail how to implement an ARIMA model in Python using the…


Added by Emmanuelle Rieuf on April 9, 2017 at 11:00am — No Comments

Introduction to Anomaly Detection

In this article, Data Scientist Pramit Choudhary provides an introduction to both statistical and machine learning-based approaches to anomaly detection in Python. Introduction: Anomaly Detection 

This overview is intended for beginners in the fields of data science and machine learning. Almost no formal professional experience is needed to…


Added by Emmanuelle Rieuf on April 6, 2017 at 12:30pm — No Comments

Implementing the Gradient Descent Algorithm in R

This article was posted by S. Richter-Walsh

A Brief Introduction: 

Linear regression is a classic supervised statistical technique for predictive modelling which is based on the linear hypothesis:

y = mx + c

where is the response or outcome variable, m is the gradient of the linear…


Added by Emmanuelle Rieuf on April 4, 2017 at 6:00pm — No Comments

Build a Recurrent Neural Net in 5 min

This video was posted on Youtube by Sirajology. He explains the basics of recurrent neural networks. Then you code your own RNN in 80 lines of python (plus white-space) that predicts the sum of two binary numbers after training.

Code for this video:…


Added by Emmanuelle Rieuf on April 4, 2017 at 12:00pm — No Comments

Book: Java Deep Learning Essentials

Book Description

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is…


Added by Emmanuelle Rieuf on April 2, 2017 at 6:31pm — No Comments

Book: Advanced R (Chapman & Hall/CRC The R Series)

An Essential Reference for Intermediate and Advanced R Programmers

Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility…


Added by Emmanuelle Rieuf on March 30, 2017 at 3:30am — No Comments

Book: Neural Networks and Statistical Learning

About the Textbook:

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches…


Added by Emmanuelle Rieuf on March 29, 2017 at 5:00pm — No Comments

What is Hadoop?

This article was posted on Intellipaat. 

Hadoop is an open-source framework developed in Java, dedicated to store and analyze the large sets of unstructured data. It is a highly scalable platform which allows multiple concurrent tasks to run from single to thousands of servers without any delay.

It consists of a distributed file system that allows transferring data and files in split seconds between different nodes. Its ability to process efficiently even if a node…


Added by Emmanuelle Rieuf on March 28, 2017 at 7:30am — 1 Comment

Apache Spark Introduction – A Comprehensive Guide for beginners

This article was posted on Data Flair. Below is a quick overview of the original article.


This tutorial provides introduction to Apache Spark, what are its ecosystem components, Spark abstraction – RDD, transformation and action. The objective of this introductory guide is to provide detailed overview of…


Added by Emmanuelle Rieuf on March 27, 2017 at 4:00pm — No Comments

Getting Started with Deep Learning

This article was written by Matthew Rubashkin. With a background in optical physics and biomedical research, Matthew has a broad range of experiences in software development, database engineering, and data analytics.

At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting…


Added by Emmanuelle Rieuf on March 24, 2017 at 12:30pm — No Comments

Machine Learning: An In-Depth Guide - Overview, Goals, Learning Types, and Algorithms

This article was written by Alex Castrounis. Alex is the founder of InnoArchiTech


Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming…


Added by Emmanuelle Rieuf on March 22, 2017 at 3:00pm — No Comments

Free Machine Learning eBooks - March 2017

Here are three eBooks available for free.


Edited by Abdelhamid Mellouk and Abdennacer Chebira

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behaviour.

Machine Learning addresses more specifically the ability to…


Added by Emmanuelle Rieuf on March 20, 2017 at 4:00pm — 5 Comments

Little Bee books: Tough topics simply explained

This is a nice collection of free eBooks to learn the ropes on topics covering Hadoop, machine learning, Spark, analytics, and more.

The Little Bee series of books provides an overview of the hot topics in data and analytics, giving you a snapshot of each technology and its potential benefit to your organisation. These books will not make you an expert, but they will improve your understanding and open the door to new ideas.

The subject of data and…


Added by Emmanuelle Rieuf on March 20, 2017 at 4:00pm — 2 Comments

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