Reinforcement Learning is about learning to make good decisions under uncertainty. It's based on the reward Hypothesis which says: That all of what we mean by goals and purposes can be well thought of as maximization of the
Digital Tranformation of Healthcare
For the past several decades the healthcare industry has been relying on manual paper-based methods for information management. Now we are entering a new era of automatic patient monitoring, smart hospitals and smart homes where services based on cloud computing
Chasing Artificial Intelligence
This is Feb 2026 the world, and there is a lot of doom and gloom about AI (and some of it rightly justified). Peter H. Diamandis is one of the more most optimistic people on the planet who thinks tech
Array Programming with Numpy
Numpy is open source numeric and scientific computations library created around 2005. Numpy is also highly popular in scientific community and has recently been used to perform computations needed to discover black holes and gravitational waves. It provides a multidimensional
Data-Driven Business Process Improvement
Process analytic approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to identify performance bottlenecks, reduce costs, extract insights and optimize the utilisation of available resources. For a long time
Strategies for Preventing model overfitting
Overftting is typically a result of using too complex models, and we need to choose a proper model complexity to achieve the optimal bias-variance tradeoff. Cross-validation and Regularization are two key techniques in machine learning to prevent overfitting. 1. Cross-validation:
What is the bias-variance tradeoff?
In this blog post we will try to cover the following questions: 1. What’s the bias-variance trade-off? 2. How’s this tradeoff related to overfitting and underfitting? 3. How do you know that your model is high variance, low
A long introduction to Machine Learning
Machine learning is closely related to Artificial intelligence * AI is about building machines or intelligent agents that exhibit intelligence. * ML enables machines to learn from experience, and is a sub-field of AI. * Deep learning focuses on a family of learning