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 (28)

• Monte Carlo integration in Python - 24 Dec 2020
  
  A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python?
• Fast and Intuitive Statistical Modeling with Pomegranate - 21 Dec 2020
  
  Pomegranate is a delicious fruit. It can also be a super useful Python library for statistical analysis. We will show how in this article.
• How to Create Custom Real-time Plots in Deep Learning - 14 Dec 2020
  
  How to generate real-time visualizations of custom metrics while training a deep learning model using Keras callbacks.
• Pull and Analyze Financial Data Using a Simple Python Package - 09 Jul 2020
  
  We demonstrate a simple Python script/package to help you pull financial data (all the important metrics and ratios that you can think of) and plot them.
• Speed up your Numpy and Pandas with NumExpr Package - 01 Jul 2020
  
  We show how to significantly speed up your mathematical calculations in Numpy and Pandas using a small library.
• Complex logic at breakneck speed: Try Julia for data science - 20 May 2020
  
  We show a comparative performance benchmarking of Julia with an equivalent Python code to show why Julia is great for data science and machine learning.
• AI and Machine Learning for Healthcare - 14 May 2020
  
  Traditional business and technology sectors are not the only fields being impacted by AI. Healthcare is a field that is thought to be highly suitable for the applications of AI tools and techniques.
• Google’s New Explainable AI Service - 20 Dec 2019
  
  Google has started offering a new service for “explainable AI” or XAI, as it is fashionably called. Presently offered tools are modest, but the intent is in the right direction.
• Build Pipelines with Pandas Using pdpipe - 13 Dec 2019
  
  We show how to build intuitive and useful pipelines with Pandas DataFrame using a wonderful little library called pdpipe.
• Activation maps for deep learning models in a few lines of code - 10 Oct 2019
  
  We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.