Kalhan's Blog

Deep learning has revolutionised technology, giving us everything from uncannily smart chatbots to medical imaging that can spot diseases better than the human eye. Yet, for a long time, a central mystery has haunted the field: why do these enormous models work so well? According to classical statistics, a model with billions of parameters—far more than its training data—should fail spectacularly. It ought to memorise the data, noise and all, and be unable to generalise to new, unseen examples. But deep neural networks defy this wisdom. They generalise brilliantly. How do we explain this apparent magic? There isn't one single answer. Instead, researchers view the problem through several different theoretical "lenses." Here are six of the most important ones. 1. The Linearisation Lens: The Neural Tangent Kernel (NTK) ⚙️ The NTK offers a startling insight: what if, under the right conditions, a massively complex neural network is just a simple, linear model in disguise? The...

Smart grids and microgrids are the future of the power system we use today. Integrating the power systems with communication devices and adding a data layer has its perks for power system operators as well as the consumers.  This novel power system will generate enormous amounts of data with its maturity. As researchers, it is quite fascinating for us since this means that we get to combine two disciplines, Machine Learning & Power Systems. In this blog post, I will list all the possible applications that I come across of machine learning in power systems. Update: I will start to categorize them into, Microgrid research Energy forecasting Smart grid research Microgrid research Multi-agent systems for microgrid control [ link ] Reinforcement learning for microgrid energy management Detect security breaches Fault detection and classification Microgrid islanding detection Multi-agent reinforcement learning for microgrid control Energy forecasting research Solar energy forecasting ...

I was interested in learning machine learning and deep learning ever since I realized its potential to apply to many research problems in power systems research. So I started learning it. So far, I have done one Coursera specialization, a course (you can probably guess this) and I'm currently doing another specialization. Before this, I went through several youtube videos to get a feel for what ML really is. Apart from this, I'm enrolled in the program by Dr. Michal Fabinger @ Tokyo Data Science My goal here is to recommend the flow of this process, which would have been very helpful in the beginning for me. Although I have given Coursera links most of the content is available open-source for free . So here it goes, Search youtube for videos that explain what machine learning is. There are a lot out there. Don't get stuck in this just take one hour that should be enough.  TensorFlow ML Zero to Hero Then start learning. A good place to start is Coursera - AMII specialization...