We are learning tokenizers because machines do not read the language as is, thus it needs to be converted to numbers and that’s where tokenizers come to the rescue. Loosely speaking tokenization is splitting sentences into words. …
How a regulated AI industry can lead to a sound human and machine relationship
So we promise you, they will never ever, ever, ever turn evil — “The Mitchells vs the machines”
If you think I am speaking in my wild imagination on what artificially intelligent devices can do to us, you are wrong.
I recently watched the movie “The Mitchells vs the machines”…
AI Alignment and Safety
It is not the technology at fault, but the intention
Responsible AI, Ethical AI, AI for social good — I am sure you must have heard these terms at some point or the other, whether you are a Data Scientist or not.
When I first heard these terms, the warning from Prof Stephen Hawking rang in my ears:
“The development…
A journey towards ethical AI
If you also carry a vision of ensuring that the product you are working on follows all the written rules of “AI for good”, then you would have definitely encountered a situation where your data is biased.
Biased models, biased data, or biased implementation — are typical woes of a…
Principles and benefits of going cloud-native
Cloud-native has changed the dynamics of the software industry and how people think of deploying and operating software applications.
As per Wikipedia, Cloud-native computing is an approach in software development that utilizes cloud computing to “build and run scalable applications in modern, dynamic environments…
Time-varying data characteristics
One of the most critical assumptions in ML data modeling is that the train and test dataset belong to similar distribution. This emphasizes the property of generalization of ML solution
Based on the generalization property, a machine learning model learns the association between the independent features and…
Degrees of freedom, statistical significance, p-value
You must be thinking why I am calling this article Binging with Statistics? Well, we all have been binging on Netflix, hot star, amazon prime and what not while struggling amid pandemic.
So, I thought to do something different and binge on some key statistics terms and share them with…
Sharing my solution to help you kickstart your hackathon journey
I recently participated in MachineHack’s Buyer’s Time Prediction Challenge and would like to share my approach with you. So, let's get started with a quick outline:
- Problem Statement
- Data understanding
- Solution: a) Target variable transformation, b) Outlier removal, c) Feature…
Sufficiency, Robustness, and more
In this article, I will talk about the various properties of a statistic.
Please see post1 for an introduction to statistical modeling and post2 to understand the difference between a statistic and an estimator.
Let’s revisit what is a statistic?
A statistic is a single value…
A quick aggregation of data quality checks
Machine Learning practitioners spend a lot of their time with data as the data quality issues inherently deter the learning of any algorithm. It follows the “Garbage in, garbage out” principle, i.e. poor data quality can only result in poor learning. …
