I am a Software Architect and an Independent Researcher who has designed and developed data products from Ideation to Go To Market at enterprise scale through my career. I am a perpetual learner who learn new things and make them work. My passion is Programming and Mathematics for Deep Learning and Artificial Intelligence. My focus area is Computer Vision and Temporal Sequences for Prediction and Forecasting.
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Posted March 23, 2025 ‐ 4 min read
Like every over-caffeinated founder with a “revolutionary” idea, I thought I was onto something BIG... saving doctors from the never-ending doom of paperwork. I mean, they signed up to save lives, not to moonlight as data entry clerks, right? So, with the confidence of someone who just watched a TED Talk, I got to work. AI-powered documentation assistant? Easy. A few late nights, gallons of coffee, and some speech-to-text magic later, I had a prototype. The feedback? “Oh wow, this is cool!” Doctors were intrigued. I was pumped. Was I the next Elon of healthcare tech? Then reality hit harder than a Monday morning. The initial hype faded, and the real question loomed: “Cool, but… will anyone actually use this?” Enter AI: not as my usual pair-programming buddy, but as my brutally honest business partner. No sugarcoating. No participation trophies. Just tough love and even tougher questions.
Posted Mar 02, 2025 ‐ 9 min read
I’ve been stuck on a problem for a while now. You know that kind of bug... the one that refuses to budge no matter how many times you rewrite the code, tweak the request payload, or double-check the backend logs. Today, I decided to try something different. Instead of debugging alone, I brought in a peer programmer... except, this time, my partner wasn’t human. Enter Claude-3.7 Sonnet-Thinking... an AI that didn’t just spit out code snippets but actually worked through the problem like a real collaborator. And trust me, this thing wasn’t just suggesting fixes... it was thinking, iterating, making mistakes, correcting them, and even rewriting parts of my backend and frontend in an attempt to solve the issue. For the first time, I felt like I was debugging with an AI, not just using one.
Posted Mar 17, 2024 ‐ 41 min read
It's been an eternity since I last endured Dr. Andrew Ng's sermon on evaluation strategies and metrics for scrutinizing the AI-generated content. Particularly, the cacophony about Large Language Models (LLMs), with special mentions of the illustrious OpenAI and Llama models scattered across the globe. How enlightening! It's quite a revelation, considering my acquaintances have relentlessly preached that Human Evaluation is the holy grail for GAI content. Of course, I've always been a skeptic, pondering the statistical insignificance lurking beneath the facade of human judgment. Naturally, I'm plagued with concerns about the looming specter of bias, the elusive trustworthiness of models, the Herculean task of constructing scalable GAI solutions, and the perpetual uncertainty regarding whether we're actually delivering anything of consequence. It's quite amusing how the luminaries and puppeteers orchestrating the GAI spectacle remain blissfully ignorant of the metrics that could potentially illuminate the quality of their creations. But let's not be too harsh; after all, we're merely at the nascent stages of transforming GAI content into a lucrative venture. The metrics and evaluation strategies are often relegated to the murky depths of technical debt, receiving the customary neglect from the business overlords.
Posted January 14, 2022 ‐ 6 min read
Ignorance is bliss. We all know there is a deeper meaning to this phrase from a philosophical context that points towards lethargic attitude. I would like to define the word ignorance as a lack of knowledge or information. Often we believe the more information we have, the more we are certain about the past, present, and future events associated with that information. Information theory differs significantly on that belief, Thanks to Claude Shannon. i.e. the more the information we have, the more we fill the uncertainty bucket that we detest. Is there any fun in knowing that an event is absolutely certain to happen? for example, Proteas won the series(Cricket) against India. The improbable state of events brings more information which is the cause for all surprises to keep us sitting on the edge of the seat. Test cricket - Game of glorious uncertainties after all..! Hence, we shall learn more about surprises especially measuring surprises.
Posted January 23, 2022 ‐ 8 min read
The definition for temperature is it is the average kinetic energy of the molecules in the space. If you find the cup of coffee your girlfriend graciously gave you this morning is not hot enough, then you can confidently conclude the molecules in the coffee pot are as lazy as you are. When the particles in the space are active, bumping into each other and have a commotion to prove their existence, we can call they are hot. What makes one hot is directly proportional to the number of particles in their space of influence traipse from a steady-state to a hyperactive one. Often these particles move aimlessly that we witness while boiling water or cooking food. This phenomenon can be understood quite clearly via Maxwell-Boltzmann distribution which is a concept from Statistical Physics/Mechanics having significant importance in machine learning and cognitive science.
This paper is going to change your perspective on AI research tangentially, if you stepping into Probabilistic DNNs. Start from here for unsupervised learning of probabilistic model using real-valued non-volume preserving transformations. Model natural images through sampling, log-likelihood and latent variable manipulations read...
This 1997 paper brings bio-physics, electro-physiology, neuroscience, differential equations etc in one place. A good starting point to understand neural plasticity, synpases, neurotransmitters, ordinary differential equations read...
Deep learning (DL) is used to interpret chest xrays (CXR) to screen and triage people for pulmonary tuberculosis (TB). This study have compared multiple DL systems and populations with a retrospective evaluation of 3 DL systems. read...
How to approach compute complexities, ie time and space complexity problems while designing a software system to avoid obvious bottlenecks in an abstract fashion. read...
Ever wonder why you repeat the same negative thoughts in your head? Why you keep coming back for more from hurtful family members, friends, or significant others? read...
Interaction with Prof. Chakra, Head of the Computational Neuroscience Lab. Computational neuroscience serves to advance theory in basic brain research as well as psychiatry, and bridge from brains to machines. watch...
Conversation with Prof. Max Welling on Deep Learning with non-Euclidean geometric data like graphs/topology or allowing networks to recognize new symmetries watch...
Yannic's review of The Lottery Ticket Hypothesis - A paper on network optimization through sub-networks. This paper is from MIT team watch...
MIT S191 Introduction to Deep Learning by Alexandar Amini and Ava Soleimany. Covers intuition to Recurrent LSTM, Attention, Gradient Issues, Sequential Modelling etc watch...
A cool explanation of Kulbuck Liebler Divergence by Kapil Sachdeva. It declutters many issues like asymmetry, loglikelihood, cross-entropy and forward/reverse KLDs. watch...
In this video, 2 PhD students are talking about overfitting and underfitting, super important concepts to understand about ML models in an intuitive way. watch...
Chariji was the third in the line of Raja Yoga Masters in the Sahaj Marg System of Spiritual Practice of Shri Ram Chandra Mission (SRCM). Shri Kamlesh Patel also known as Daaji, is the current Guide of Sahaj Marg System (known today as HEARTFULNESS ) and is the President of Shri Ram Chandra Mission. watch...