Frequentist or Bayesian, Who am I?

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.

Selected Reads Selected Watch More About Me

Selected Writes - AI, ML, Math

Relooking Attention Models, SHA-RNN Overview, gMLP Briefing and Measuring Efficacy of Language Models through Perplexity

Satire and sarcasm are seldom seen in scientific writing but this is an era of memes and trolls where complex concepts are conveyed through highly comprehensible mediums(videos, animations, etc). When it comes to being critical(without hurting) about a concept or a character, sarcasm is taken as a medium in the literary renditions but seldom do we see them in the scholarly scriptures. Such sui generis is convivial and fervent for the patrons - Stephen Merity's 2019 paper titled Single Headed Attention RNN: Stop Thinking With Your Head(SHA-RNN) is one such scholarly writing where he is critical about today's (leading) approaches in language modeling especially our obsession towards the Attention Models without demonstrating outrage or distress. His paper is lucid and takes us back to celebrate the glory of yesteryear's multi-layer perceptrons. A more recent paper(Jun 2021) from Google titled Pay Attention to MLPs(gMLP) periphrastically confirms Stephen's claims with substantial empirical proof.

Gossips and Epicenter of Emotions for our Existence - Study on Anatomy of Limbic System

Cognitive and communicative systems of human brain did not evolve to build mathematical models or to find philosophical insights during our primitive times - They evolved so that we can gossip. Gossipping is the fundamental attribute of human beings that made us who we are. Gossips enabled men to create languages, cultures and civilizations, do you know for what? to impress our respective girlfriends(and boyfriends, obviously) and partners - makes it the epicenter for our existence. Why do I call Gossips the epicenter of our existence - Through gossips we conveyed our emotions and feelings that are seldom spoken openly, however spoken definitely that changed the course of our history - from Helen of Troy to Monica Lewinsky, we gossipped and gossipped until the reign brought down to its knees(pun intended). One single act called gossip designed the destiny of humanity by producing flavors of emotions in the human brain, specifically in the limbic region. That region has to be studied because our quest is to build human like intelligence on a silicon wafer and emotions are the prime factor that makes a human human.

Methodus Fluxionum et Serierum Infinitarum - Numerical Methods for Solving ODEs Using Our Favorite Tools

Wikipedia says, a differential equation is an equation that relates to one or more functions and their derivatives. In layman's term, the only constant in this life(universe) is change and any entity that is capable of adapting to change especially threats and adversarial ones thrived and flourished - Hence we are interested in studying the change and the rate at which the change occurs. Uff, that is too layman-ish definition for differential equations even for an unscholarly writer of my kind. Apparently, Newton called those functions fluxions, Gottfried Wilhelm Leibniz independently identified them are all history - they made differential equations a compelling topic for understanding the nature. Further, numerical analysis is a way to solve equations of algebraic order, they are quite the functions of convergence in the quest for achieving intelligence(artificial).

A Practical Guide to Univariate Time Series Models with Seasonality and Exogenous Inputs using Finance Data of FMCG Manufacturers

The definition of univariate time series is, a time series that consists of single scalar observations recorded sequentially over equal periodic intervals. i.e An array of numbers are recorded where time is an implicit dimension represented at constant periodicity. Univariate time series models(UTSM) are the simplest models that allow us to forecast the future values by learning the patterns in the sequence of observations recorded. The key elements of these patterns are Seasonality, Trends, Impact Points and Exogenous Variables. There are 3 schemes of pattern identification acts as building block for UTSMs, they are auto regression(OLS), moving averages and seasonality - When they augmented with external data, effectiveness of the model improves significantly.

Trend, Features of Structural Time Series, Mathematical Intuition Behind Trend Analysis for STS

Decomposability is the prime factor for the success of Generalized Additive Models in the quest for forecasting future events from the observed dataset. When we design a time series forecasting model, the functional features that we often observe from the data are trend, seasonality and impact points. The decomposable nature of these features from the dataset makes the problem conducive to model individual features independently by considering it as a curve fitting exercise. i.e. Modeling trend independently from other features makes the outcomes interpretable and subsequently paves way for advantages like bringing analyst in the loop in attaining convergence. This approach ignores the explicit dependence of temporal structure in the data that is a common function in generative models like ARIMA.


Selected Reads - Papers, Articles, Books

Density Estimation using Real NVP - GOOGLE RESEARCH/ICLR

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...

The Neural Code between Neocortical Pyramidal Neurons Depends on Neurotransmitter Release‚ÄČProbability - PNAS

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...

Using AI to read Chest X-Rays for Tuberculosis Detection and evaluation of multiple DL systems - NATURE

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...

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization - IEEE/ICCV

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...

Evolve Your Brain: The Science of Changing Your Mind by Joe Dispenza - BOOK

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...

Selected Watch - Social Media/OTT Content

Eureka : Dr V. Srinivasa Chakravarthy, Prof, CNS Lab,IITM

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...

Quantum, Manifolds & Symmetries in ML

Conversation with Prof. Max Welling on Deep Learning with non-Euclidean geometric data like graphs/topology or allowing networks to recognize new symmetries watch...

The Lottery Ticket Hypothesis

Yannic's review of The Lottery Ticket Hypothesis - A paper on network optimization through sub-networks. This paper is from MIT team watch...

Backpropagation through time - RNNs, Attention etc

MIT S191 Introduction to Deep Learning by Alexandar Amini and Ava Soleimany. Covers intuition to Recurrent LSTM, Attention, Gradient Issues, Sequential Modelling etc watch...

What is KL-Divergence?

A cool explanation of Kulbuck Liebler Divergence by Kapil Sachdeva. It declutters many issues like asymmetry, loglikelihood, cross-entropy and forward/reverse KLDs. watch...

Overfitting and Underfitting in Machine Learning

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...

Attitude ? Explains Chariji - Pearls of Wisdom - @Heartfulness Meditation

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...