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Janardhanan PS
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Janardhanan PS updated their profile
yesterday
Janardhanan PS's blog post was featured

Ability to generalize - A measure of intelligence ?

Knowledge acquisition is about building generalization capabilities. In machine learning world, generalization refer to the model's ability to make accurate predictions from never before seen data. Well generalized models possess intelligence to work on data from new scenarios. This is true for human intelligence also. Children start learning from examples and initially they fail to respond properly to unforeseen situations. Gradually they acquire generalization skills to respond to all…See More
Feb 20
Janardhanan PS posted a status
"Adopting Machine Learning in IT solutions"
Feb 19
Janardhanan PS posted a status
"What are you up to?"
Feb 16
Janardhanan PS's blog post was featured

Overcoming surprises faced by Programmers migrating to Machine learning projects

Traditional programmers hit a stone-wall when they start working on Machine Learning (ML) development projects. They were used to coding based on design derived from requirements, logic/rules and when they move into a new paradigm in which rules are automatically generated from data, they are surprised. In ML paradigm, we do not find any output code and we see an output in the form of a binary object called model that goes into production. You may be wondering without measuring lines of code,…See More
Feb 13
Janardhanan PS and Ramesh Dontha are now friends
Feb 10
Janardhanan PS's blog post was featured

Human Learning and Machine Learning - How they differ ?

Is there any similarity in learning process between humans and machines. Human learning process varies from person to person. Once a learning process is set into the minds of people, it is difficult to change it. But, in Machine Learning (ML), it is easy to change the learning method by selecting a different algorithm. In ML, we have well defined processes to understand and estimate the accuracy in learning. Estimation of human learning is usually done through examinations and it cannot be…See More
Feb 6
Janardhanan PS posted a blog post

Does "All models are wrong, but some are useful" quote apply to Machine learning models?

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…See More
Jan 24
Lance Norskog commented on Janardhanan PS's blog post Does "All models are wrong, but some are useful" quote apply to Machine learning models?
"More useful is- "no model is perfect, so you must have a back-up plan". I saw a company deploy face recognition for commercial truck fleets, to verify the driver sitting behind the wheel. The system had no back-up plan for that one guy who…"
Jan 23
Tim Matteson liked Janardhanan PS's blog post Does "All models are wrong, but some are useful" quote apply to Machine learning models?
Jan 23
Janardhanan PS's blog post was featured

Does "All models are wrong, but some are useful" quote apply to Machine learning models?

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…See More
Jan 22
Janardhanan PS liked Janardhanan PS's blog post Overcoming Barriers in Machine Learning adoption in corporate world
Jan 20
Vincent Granville commented on Janardhanan PS's blog post Importance of Hyper-parameters in Model development
"I would the parameter K in  K-fold cross validation and K in K-nearest neighbors."
Jan 16
Janardhanan PS's blog post was featured

Importance of Hyper-parameters in Model development

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from ML…See More
Jan 16
Janardhanan PS posted a blog post

Overcoming Barriers in Machine Learning adoption in corporate world

We have data-driven decision support systems implemented in Management Information Systems(MIS). Algorithms created by humans coded into MIS chew raw data and spit out decisions. The MIS systems were developed by human with substantial effort in software development. Once created, they allowed very little flexibility in deriving insights from new data sets. Now we have Machine Learning (ML) systems capable of making data-driven decisions or predictions without the need for explicit programming.…See More
Jan 9

Profile Information

Short Bio
An IT professional with 39 years of experience. Current areas of interest are , Machine Learning Software Development, Big Data processing, NoSQL databases
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/janardhanan-ps-7a30b71
Field of Expertise
Data Science, Machine Learning, AI, Deep Learning
Professional Status
Technical
Years of Experience:
39
Your Company:
SunTec Business Solutions Pvt Ltd.
Industry:
Application Software
Your Job Title:
Machine Learning Evangelist
How did you find out about DataScienceCentral?
email
Interests:
Networking, New venture, Recruiting

Janardhanan PS's Blog

Ability to generalize - A measure of intelligence ?

Posted on February 16, 2020 at 9:02pm 0 Comments

Knowledge acquisition is about building generalization capabilities. In machine learning world, generalization refer to the model's ability to make accurate predictions from never before seen data. Well generalized models possess intelligence to work on data from new scenarios. This is true for human intelligence also. Children start learning from examples and initially they fail to respond properly to unforeseen situations. Gradually they acquire generalization skills to respond to all… Continue

Overcoming surprises faced by Programmers migrating to Machine learning projects

Posted on February 10, 2020 at 7:14pm 0 Comments

Traditional programmers hit a stone-wall when they start working on Machine Learning (ML) development projects. They were used to coding based on design derived from requirements, logic/rules and when they move into a new paradigm in which rules are automatically generated from data, they are surprised. In ML paradigm, we do not find any output code and we see an output in the form of a binary object called model that goes into production. You may be wondering without measuring lines of code,… Continue

Human Learning and Machine Learning - How they differ ?

Posted on February 4, 2020 at 8:43pm 0 Comments

Is there any similarity in learning process between humans and machines. Human learning process varies from person to person. Once a learning process is set into the minds of people, it is difficult to change it. But, in Machine Learning (ML), it is easy to change the learning method by selecting a different algorithm. In ML, we have well defined processes to understand and estimate the accuracy in learning. Estimation of human learning is usually done through examinations and it cannot be… Continue

Does "All models are wrong, but some are useful" quote apply to Machine learning models?

Posted on January 20, 2020 at 11:30pm 1 Comment

Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…

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