In the series of implementing Recommendation engines, in my previous blog about recommendation system in R, I have explained about…
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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
Imagine yourself relocating to a more industrial place for living because your beach house was washed away in the tide. In another scenario imagine yourself wearing masks throughout the year. What if I say that, all this that you just imagined could be a reality very soon?
Whether you choose to believe it or not, climate change is happening for real. Even though you might not be able to spot a lot of its impact around you, the world…Continue
Added by Divyesh Aegis on December 2, 2019 at 11:24pm — 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
Previously, we saw how unsupervised learning actually has built-in supervision, albeit hidden from the user.
In this post we will see how supervised and unsupervised learning algorithms share more in common than the textbooks would suggest. As a matter of fact, both classes can use identical…Continue
Added by Danko Nikolic on September 23, 2018 at 1:34pm — No Comments
Figure 1. Scatter plot of word embedding coordinates (coordinate #3 vs. coordinate #10). You can see that semantically related words are close to each other.
This blog post is an extract from chapter 6 of the book “From Words to Wisdom. An Introduction to Text Mining…Continue
Added by Rosaria Silipo on May 7, 2018 at 12:00am — 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
Machine Learning is being hailed as “Next Generation Analytics”.Machine Learning tasks can be roughly classified as –
Added by Ivy Pro School on April 11, 2016 at 5:57am — 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