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All Blog Posts Tagged 'machine' (189)

Can DeepMind win 'Jeopardy' and Watson win 'Go'?

original Post: linkedin

We are indeed living in interesting times, where we celebrate human-built machines defeating the best human minds at variety of activities. IBM Deep Blue's win against Chess champ…

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Added by Ashish kumar on April 10, 2016 at 10:24am — No Comments

Machine Learning : Few rarely shared trade secrets

Some of the rarely shared trade secrets in machine learning:   Original post: on linkedin

1. Bootstrap sampling & the magic number 0.63

Even though randomly sampled,…

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Added by Ashish kumar on April 1, 2016 at 9:00am — No Comments

XGBoost: A Scalable Tree Boosting System

"Abstract Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and…

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Added by Diego Marinho de Oliveira on March 14, 2016 at 7:02am — 1 Comment

AirBnB New User Bookings, Kaggle Winner's Interview: 3rd Place

AirBnB New User Bookings was a popular recruiting competition that challenged Kagglers to predict the first country where a new user would book travel. This was the first recruiting competition on Kaggle with scripts enabled. AirBnB…

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Added by Diego Marinho de Oliveira on March 10, 2016 at 2:30am — No Comments

Feature engineering? Start here!

One of the hot topics on Machine Learning is, with no doubts, feature engineering. In fact, it comes before the buzz on this topic, simple when we talk about Data Mining. Remembering the CRISP-DM process, feature engineering (and, consequently, feature selection) is the core of a great data mining project – it comes to life on the Data Preparation phase, that is the task to have constructive data preparation operations such as the production of derived attributes or entire new records, or…

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Added by Leandro Guerra on February 15, 2016 at 12:30am — No Comments

Step-by-step video courses for Deep Learning and Machine Learning

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! …

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Added by LazyProgrammer.me on January 23, 2016 at 8:30pm — 2 Comments

5 predictions for 2016 on data, analytics and machine learning

The first prediction is that data and analytics will continue to grow at an astounding pace and with increased velocity

This is no big surprise as all the past reports have pointed towards this growth and expansion -…

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Added by Bruce Robbins on January 3, 2016 at 5:00am — No Comments

The Data-Driven Weekly #1.2

Last week witnessed a number of exciting announcements from the big data and machine learning space. What it shows is that there are still lots of problems to solve in 1) working with/deriving insights from big data, 2) integrating insights into business processes.



TensorFlow

Probably the biggest (data) headline was that Google open sourced TensorFlow, their graph-based…

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Added by Brian Rowe on November 17, 2015 at 6:02am — No Comments

What Is Unsupervised Learning?

 Unsupervised learning algorithms are machine learning algorithms that work without a desired output label. A supervised machine learning algorithm typically learns a function that maps an input x into an output y, while an unsupervised learning algorithm simply analyzes the x’s without requiring the y’s. Essentially, the algorithm attempts to estimate the underlying structure of the population of x’s (in other…

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Added by Aureus Analytics on November 16, 2015 at 10:00pm — No Comments

Handbook of Research on Ubiquitous Machine Learning and its Applications - IGI Global

I'm currently in the process of editing a book entitled "Handbook of Research on Ubiquitous Machine Learning and its Applications" by IGI Global. This book will cover research works on the topics of Machine Learning, Data Mining, Clustering, Classification, etc, and it will be published by the end of 2016.
 
 
The call for proposals is now open: …
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Added by Neuza Nunes on October 23, 2015 at 11:57am — No Comments

10 Popular Java Machine Learning Tools & Libraries

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Added by Demnag on September 13, 2015 at 8:07pm — No Comments

Training Neural Networks: Q&A with Ian Goodfellow, Google

Neural networks require considerable time and computational firepower to train. Previously, researchers believed that neural networks were costly to train because gradient descent slows down near local minima or saddle points. At the RE.WORK Deep…

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Added by Sophie Curtis on September 3, 2015 at 8:59am — No Comments

Data Science and Technology Monthly - August 2015

Hello and Welcome back!

This series is my attempt to start cataloging all the interesting articles, industry reports, whitepapers, and news that I read every month, related to technology and data science. We are at Month 2 and let us dig right in -

1. Network Effects, Digital Avatars and Advertising

This essay titled "…

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Added by Srividya Kannan Ramachandran on August 17, 2015 at 5:30am — No Comments

Machine Learning in Javascript- A compilation of Resources

One of the beauties of running Javascript related applications is you don’t need to install any client side software, optimize servers and spend tons of time on the core infrastructure. Javascript just work outs of the core browser. In that spirit, there is a lot of increasing momentum on building Machine Learning in Javascript. We have collected a list of resources on Javascript…

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Added by Vozag on August 6, 2015 at 9:30pm — No Comments

Machine learning is not better than Human learning.

Alan Turing was the first one to present the idea of simulating the machine thinking. Its been more than 60 years since the ground breaking paper of Alan Turing came out, The Imitation Game. The world has changed rapidly since then. 

The machines of today have become so powerful. They can actually think, which endorses the idea of Alan Turing presented in 50s. However, the machine thinking may be different. Alan Turing argued, just because the thinking can be…

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Added by Rana Usman on June 30, 2015 at 12:54pm — 2 Comments

Ten General Principles in Data Mining/Science

Through years, working with different clients and applications, I have found a set of data mining general principles that also hold through in the context of big data.  These are all listed in my book. Here I enumerate them using the terminology I have used in the book:
  1. Use of “all the data” is not equivalent to building the deepest Analytics Dataset (ADS) in terms of…
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Added by Khosrow Hassibi on April 3, 2015 at 9:30am — 1 Comment

Get Started with the Data Science Bowl

We’ve created a Domino project with starter code in R and Python for participating in the Data Science Bowl. 

Get a jump start in the competition with our starter project by training your models on massive hardware and running multiple experiments in parallel while keeping track of…

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Added by Anna Anisin on January 13, 2015 at 3:00pm — No Comments

How-to use Bag of little bootstraps Methodology to Compute Error Bounds on Machine Learning Tasks

We all know that calculating error bounds on metrics derived from very large data sets has been problematic for a number of reasons. In more traditional statistics one can put a confidence interval or error bound on most metrics (e.g., mean), parameters (e.g., slope in a regression), or classifications (e.g., confusion matrix and the Kappa statistic).

For many machine learning applications, an error bound could be very important.…

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Added by Anna Anisin on December 14, 2014 at 3:33pm — No Comments

Don’t Judge a Tweet by its 140 Characters: How One App is Using Machine-Learning to Tackle Credibility on Twitter

When you use Twitter, how do you know when you are being presented with something credible instead of something totally bogus? The answer is, unless you spend a lot of time researching each tweet, you probably don’t. However, one thing is for certain, we rely on what we read on Twitter to be true.

Twitter is one of the fastest and most effective ways we disseminate news across our world. If this…

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Added by Renette Youssef on December 8, 2014 at 4:00pm — No Comments

Apache Spark: distributed data processing faster than Hadoop

This blog is extrapolated from DataScience Hacks by the author himself. 

Apache Spark, another apache licensed top-level project that could perform large scale data processing way faster than Hadoop (I am referring to MR1.0 here). It is possible due to Resilient Distributed Datasets concept that is behind this fast data processing. RDD is basically a collection of objects,…

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Added by Pavan Kumar on September 28, 2014 at 7:00am — 1 Comment

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