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

Istio Service Mesh, Canary Release Routing Strategies for ML Deployments in a Kubernetes Cluster

Change is the only constant thing in this universe. Our data changes and cause data drift then the understanding of the nature change and cause concept drift. However, we believe building State of the Art(SOA), One of a Kind(OAK), and First, of its Time(FOT) in-silico intelligence will achieve a nirvana state and juxtapose us next to the hearts that are liberated from the cycle of life and death. Constructing a model is just the end of the inception, real trials of difficulty and the excruciating pain of managing changes are awaiting us. Shall we plan well ahead by having a conscious focus on a minimum viable product that promises a quicker time to market with a fail-fast approach? Our ego doesn't allow that because we do not consider software development is cool anymore, we believe building intelligence alone makes us deserving our salt. Today anyone can claim themselves a data scientist because of 2 reasons. Until 2020 we wrote SQL queries for existence. It is 2021 - Covid bug bit and mutated us, we survived variants and waves that naturally upgraded the SQL developer within to a data scientist(evolutionary process). Reason 2 - With all due respect to one man Dr.Andrew Ng, with his hard work and perseverance, made us believe we are all data scientists. By the way, they say ignorance is bliss and we can continue building our SOA, OAK, and FOT models forever at the expense of someone's cash. BTW, Anyone noticed Andrew is moving away from the model-centric AI to the data-centric AI - He is a genius and he will take to the place we truly belong.

Atoms and Bonds 2 - ML for Predicting Quantum Mechanical Properties of Organic Molecules

It is enthralling to see machine learning algorithms solve core science problems, It enables us to revisit favorite subjects after years and for few even decades. Like any other field, ML had a humble beginning by detecting cats, dogs, and their respective mothers-in-law. Drug discovery is a prolonged and pricey process, Pharmaceutical firms research with two kinds of molecules to increase the efficacy of the drug in its entirety. One is the source molecule(the drug), the other is the target molecule where the drug has to act upon, also the target molecules have their peripheral molecules to act upon. The quest is to predict the biochemical activity(atomization) between the compounds quantitatively and qualitatively for the cure with no side effects. Machine learning algorithms help in the process of investigating a huge library of chemical compounds and test their biochemical impact on the target molecules.

Atoms and Bonds - Graph Representation of Molecular Data For Drug Detection

In computer science, a graph is a powerful data structure that embodies connections and relationships between nodes via edges. A graph illustration of information strongly derives its inspiration from nature. We find a graph or graph-like formations everywhere in nature, from bubble foams to crushed papers. E.g. the cracked surfaces of a dry riverbed or a lake-bed during the dry season is a specialized topological design of graph data structure called Gilbert Tesselations. Soap bubbles and foams form double layers that separate the films of water from pockets of air made up of complex forms of curved surfaces, edges, and vertices. They form the bubble clusters and these clusters are represented as Mobius-invariant power diagrams, one another special kind of graph structure. Conventional DL algorithms restrict themselves to the tabular or sequential representation of data and lose their efficacy. However, Message Passing NN architecture(MPNN) is a Graph Neural Network(GNN) scheme where we can input graph information without any transformations. MPNNs are used in the field of drug detection and it inspired we can model molecular structures for penetrating blood-brain barrier membrane.

Graph Convolution Network - A Practical Implementation of Vertex Classifier and it's Mathematical Basis

Traditional deep learning algorithms work in the Euclidean space because the dataset is transformed and represented in one or two dimensions. This approach results in loss of information, especially on the relationship between two entities. For example, the network organization of the brain suggests that the information is stored in the neuronal nexus. i.e Neurons fire together, wire together - Hebbian Theory. The knowledge of togetherness or relationships can be ascertained strongly in the non-Euclidean space in the form of Graphs Networks. Such intricate graph networks are evolved to maximize efficiency and efficacy in the form of information transfer at a minimum cost(energy utilization) to accomplish complex tasks. Though the graph networks solve the spatial challenges to certain extents, temporal challenges are yet to be addressed. Extending DNN theories with the graph is the current trend, e.g. an image can be considered as a specialized graph where the pixels have relation to their adjacent ones, to perform a Graph Convolution.

Introduction to Graph Neural Networks

Information stored and fed to deep-learning systems are either in the tabular format or in the sequential format, this is because of our antiquated way of storing data in relational database design inspired by pre-medieval accounting systems. Though the name has the word relation, the actual relationships are established independent of the data(e.g. across tables through P/F keys). This is an un-intuitive and in-efficient way of representation that guarantees convenience for a computer programmer's comprehension but not the needs of the machine-assisted, data-driven lifestyle of today. The inherent nature of the human cognitive system is the ability to comprehend the relationship and store them as relationship (graphically or hierarchically) ensures supremacy in the creation of ideas, retrieval of memories, modification to beliefs, and removal of dogmas(arguably). On contrary, current (leading) approaches in data storage are tabular or linear - could be the cause for inefficiency in achieving convergence despite the consumption of very high energy(compared to the animal brain) to achieve simple tasks. I spent some time with graphs, graph neural networks(GNN), and their architecture to arrive at the above intuition. I believe GNNs are bringing us a little closer to building human-like intelligent systems inspired by the human way of storing information.


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...