About Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar is a Sr. Principal Engineer at ON Semiconductor, where he works on stateoftheart semiconductor technology development and applies AI/ML techniques for design automation, AIcentered hardware development, and predictive analytics. He contributes regularly to publications such as KDnuggets and TDS on diverse topics related to data science and machine learning. He has authored data science books and contributes to open source software. Tirthajyoti holds a Ph.D. in EE and is working on an M.S. degree in Computational Data Analytics. Email him at tirthajyoti at gmail[dot]com.
Tirthajyoti Sarkar Posts (24)

How a simple mix of objectoriented programming can sharpen your deep learning prototype  01 Aug 2019
By mixing simple concepts of objectoriented programming, like functionalization and class inheritance, you can add immense value to a deep learning prototyping code.

How do you check the quality of your regression model in Python?  02 Jul 2019
Linear regression is rooted strongly in the field of statistical learning and therefore the model must be checked for the ‘goodness of fit’. This article shows you the essential steps of this task in a Python ecosystem.

Optimization with Python: How to make the most amount of money with the least amount of risk?  26 Jun 2019
Learn how to apply Python data science libraries to develop a simple optimization problem based on a Nobelprize winning economic theory for maximizing investment profits while minimizing risk.

Mathematical programming — Key Habit to Build Up for Advancing Data Science  15 May 2019
We show how, by simulating the random throw of a dart, you can compute the value of pi approximately. This is a small step towards building the habit of mathematical programming, which should be a key skill in the repertoire of a budding data scientist.

Linear Programming and Discrete Optimization with Python using PuLP  08 May 2019
Knowledge of such optimization techniques is extremely useful for data scientists and machine learning (ML) practitioners as discrete and continuous optimization lie at the heart of modern ML and AI systems as well as datadriven business analytics processes.

What are Some “Advanced” AI and Machine Learning Online Courses?  22 Feb 2019
Where can you find notsocommon, but highquality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?

Synthetic Data Generation: A musthave skill for new data scientists  27 Dec 2018
A brief rundown of methods/packages/ideas to generate synthetic data for selfdriven data science projects and deep diving into machine learning methods.

When Bayes, Ockham, and Shannon come together to define machine learning  25 Sep 2018
A beautiful idea, which binds together concepts from statistics, information theory, and philosophy.

Essential Math for Data Science: ‘Why’ and ‘How’  06 Sep 2018
It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.

Why You Should Start Using .npy Files More Often  03 Apr 2018
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.