About Tirthajyoti Sarkar

Dr. Tirthajyoti Sarkar is a Sr. Principal Engineer at ON Semiconductor, where he works on state-of-the-art semiconductor technology development and applies AI/ML techniques for design automation, AI-centered 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 object-oriented programming can sharpen your deep learning prototype - 01 Aug 2019
  
  By mixing simple concepts of object-oriented 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 Nobel-prize 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 data-driven business analytics processes.
• What are Some “Advanced” AI and Machine Learning Online Courses? - 22 Feb 2019
  
  Where can you find not-so-common, but high-quality online courses (Free) for ‘advanced’ machine learning and artificial intelligence?
• Synthetic Data Generation: A must-have skill for new data scientists - 27 Dec 2018
  
  A brief rundown of methods/packages/ideas to generate synthetic data for self-driven 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.