In the series of implementing Recommendation engines, in my previous blog about recommendation system in R, I have explained about…
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If you team is evaluating privacy-preserving synthetic data, three factors will usually come into consideration:
This post focuses on the last factor: the value of adding synthetic data generation capabilities to your team’s…
Added by Elise Devaux on October 12, 2020 at 3:00am — No Comments
10 use-cases for privacy-preserving synthetic data
Fast-evolving data protection laws are constantly reshaping the data landscape. The organizational ability to overcome sensitive data usage restrictions while safeguarding customer privacy will be a key driver of tomorrow’s successful businesses. This blog presents ten concrete applications for privacy-preserving synthetic…Continue
Added by Elise Devaux on August 5, 2020 at 6:59am — No Comments
This blog takes a closer look at the concept of privacy-preserving synthetic data. It answers the question “what is synthetic data” and looks at the origin of synthetic data in the context of data privacy. It also presents one way of generating privacy-preserving synthetic data and its benefits for organizations.…Continue
Added by Elise Devaux on July 2, 2020 at 11:30am — No Comments
As I learn about data privacy, I’m starting to realize how large the ecosystem is. I focused here on a category that spans across the data privacy landscape, Privacy Enhancing Technologies (PETs). In the post, I cover:…
Added by Elise Devaux on June 12, 2020 at 2:14am — No Comments
Added by Elise Devaux on May 23, 2020 at 1:00pm — No Comments
By 2022, Gartner predicts that 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
“Increasingly, leading and thriving organizations in every segment are wielding data and analytics as a competitive weapon, operational accelerant, and innovation catalyst,” notes analysts in …
Added by Tricia Morris on February 19, 2020 at 8:26am — No Comments
Properly implemented Machine Learning (ML) models can have a positive effect on organizational efficiency. It is first necessary to understand how these models are created, how they function, and how they are put into production.
The Definition of a Machine Learning Model
When a computer is presented with questions within a particular domain, a machine learning model will run an algorithm that will enable it to resolve those questions. These algorithms are not…Continue
We all know that deep learning algorithms improve the accuracy of AI applications to great extent. But this accuracy comes with requiring heavy computational processing units such as GPU for developing deep learning models. Many of the machine learning developers cannot afford GPU as they are very costly and find this as a roadblock for learning and developing Deep learning applications. To help the AI, machine learning developers Google has released…
Added by suresh kumar Gorakala on October 1, 2018 at 9:07am — No Comments
One of the first lessons you’ll receive in machine learning is that there are two broad categories: supervised and unsupervised learning. Supervised learning is usually explained as the one to which you provide the correct answers, training data, and the machine learns the patterns to apply to new data. Unsupervised learning is (apparently) where the machine figures out the correct answer on its own.
Supposedly, unsupervised learning can discover something new that has not been found…Continue
Added by Danko Nikolic on February 14, 2018 at 1:00pm — No Comments
This post is the third part of the multi-part series on how to build a search engine –
Added by Vivek Kalyanarangan on December 30, 2016 at 6:00am — No Comments
Deep learning is all the rage. You hear about it in the news, you read it about it in the news and it’s all over popular culture as well. What’s more, it’s revolutionizing the tech industry, as computers…Continue
Added by Malia Keirsey on December 5, 2016 at 12:00pm — No Comments
Most people think data science is smart people doing very smart stuff. Well that’s not it. Data science is just another subject involving its own bit of subtle complexities that has to be handled with knowledge and an innovative approach. JUST LIKE COOKING.
Cooking is art and science. So is Analytics. Both start from getting the right ingredients. No matter how many spices and cooking techniques you apply, the dish won’t…Continue
Added by Vivek Kalyanarangan on November 1, 2016 at 10:00am — No Comments
"Information is the oil of the 21st century, and analytics is the combustion engine" Peter Sondergaard, SVP, Gartner Research
In analytics, we retrieve information from various data sources; it can be structured or unstructured. The biggest challenge here is to retrieve information from unstructured data mainly texts. Here machine learning comes into the picture to overcome this challenge. Different algorithms have been designed in different platforms…Continue
Added by Vivek Kalyanarangan on September 9, 2016 at 8:30am — No Comments
As R programming language becoming popular more and more among data science group, industries, researchers, companies embracing R, going forward I will be writing posts on learning Data science using R. The tutorial course will include topics on data types of R, handling data using R, probability theory, Machine Learning, Supervised – unSupervised learning, Data Visualization using R, etc. Before going further, let’s just see some stats and tidbits on data science and…Continue
Added by suresh kumar Gorakala on November 23, 2015 at 7:04pm — No Comments
I’m going to keep this tutorial light on math, because the goal is just to give a general understanding.
The idea of Monte Carlo methods is this—generate some random samples for some random variable of interest, then use these samples to compute values you’re interested in.
I know, super broad. The truth is Monte Carlo has a ton of different applications. It’s…Continue
Random Forest is a machine learning algorithm used for classification, regression, and feature selection. It's an ensemble technique, meaning it combines the output of one weaker technique in order to get a stronger result.
The weaker technique in this case is a decision tree. Decision trees work by splitting the and re-splitting the data by…Continue
Added by Alex Woods on July 4, 2015 at 8:30am — No Comments
In my last post, I have explained about MSE, today I will explain the variance & bias trade-off, Precision recall trade-off while assessing the model accuracy.
Variance refers to the amount by which the estimated output (f) would change if we estimated it (f) using a different training dataset. Since the training data is used to fit the statistical learning method, different training sets will…
Added by suresh kumar Gorakala on August 5, 2014 at 6:24am — No Comments
Statistics.com, a provider of online education in statistics and analytics, announces a partnership with CrowdANALYTIX, a predictive modeling “managed crowdsourcing” company, offering a new online course, “Applied Predictive Analytics in partnership with CrowdANALYTIX“, which will run from Oct. 11 to Nov 8, 2013.
The goal of this course is to teach users (who have basic knowledge of R programming, predictive analytics…Continue
Added by Janet Dobbins on September 11, 2013 at 10:59am — No Comments
Big Data holds a big promise. But has that promise paid out already? Or are you heading for Big Dollar Disaster? Many take inventory of their data and find out they have terabytes of data lying around. Surely something should be done with that, so here’s how we see a lot of companies going about implementing ‘something’ for their Big Data.
Added by Jos Verwoerd on November 13, 2012 at 4:04am — No Comments