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

Do You Know We Can Approximate Any Continuous Function With A Single Hidden Layer Neural Network - A Visual Guide

Ok, that is neither true nor false. Theoretically speaking, a single hidden layer neural network can approximate any continuous function in the 1-d space with few caveats like a. fail to generalize b. no learnability and c. impossibly large layer size. However, there is a guarantee that neural networks can approximate any continuous function for every possible input whether they are single input ones or multiple inputs. There is a universality when it comes to neural networks. This universal property of neural networks makes deep learning models work reasonably well for almost any complex problem. We are in the early stage of deep learning development, with current evolution we are generating text descriptions for image input, translating Swahili sentences into Japanese equivalents, create faces that never exist before. In this post, we shall study the nuances of the Universal Approximation Theorem for Neural Networks, a fundamental property of deep learning systems in detail.

That Straight Line Looks a Bit Silly - Let Us Approximate A Sine Wave Using UAT

This is the continuation of my first post on the Universal Approximation Theorem. My previous post took a simple case of approximating a leading straight line and in this post, we are approximating a sinewave using numpy that is smoothed using a Gaussian Filter.

Deep Learning is Not As Impressive As you Think, It's Mere Interpolation

This post is a mere reproduction(with few opinions of mine) of one of the interesting discussions of Deep Learning focusing on interpolation/extrapolation in Twitter. The whole discussion was started because of an interesting reply from Dr.Yann LeCun to Steven Pinker who made an appreciation note to Andre Ye's post titled - You Don’t Understand Neural Networks Until You Understand the Universal Approximation Theorem.

Survival Analysis using Lymphoma, Breast Cancer Dataset and A Practical Guide to Kaplan Meier Estimator

I live in the hills of Kumaon, the Himalayas a region of biodiversity, scenic beauty, and fertile landscapes situated to the west of Nepal. This region is full of fruit orchards that thrive due to the conducive condition and the fertile soil. An interesting aspect that caught my attention is the farmers allowing frugivores in their orchards to consume their produce that results in an effective seed-dispersing scheme. Most of the frugivores have a specialized digestive system to process fruits and leave the seeds intact from their gut. The quest is how long the farmers have to leave the fleshy fruits in their orchards so that the animals consume them for an effective seed-dispersing process. Statistics have the answer, Survival analysis is a methodology widely used in medical research to measure the probability of patients living after a certain amount of time after the treatment for a disease. It comes under the medical prognosis scheme of healthcare. Using survival analysis we can find how long the fleshy fruits have to remain in the tree for the frugivores to consume.

Dequantization for Categorical Data, Categorical NFs via Continuous Transformations - A Paper Review

Of late we handle and store almost all of the information that humanity creates in digital format, that is in-silico bits of the discrete order. However, every aspect of nature and the laws that govern nature are continuous. When we say all the information, we truly did not mean ALL the information but the information we believe that is relevant. Also, that information that we are capable of capturing. If that is confusing, we need infinite energy and storage for all the confounders of an event that we do not have. Someone naively said a butterfly flapping its wings can cause a typhoon but there is a small shred of wisdom in it, small events do serve as catalysts that act on starting conditions. We cannot capture and store all those high dimensional events but we can study the deep distributions caused by them by transforming from discrete space to continuous space. The process of casting encodings of categorical data from the discrete space to continuous space is called dequantization, this process allows us to create flexible distributions of high dimensional data to build robust machine learning models.


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 Probability - 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...