As AI systems grow in scale and complexity, traditional monolithic neural networks often fall short in adaptability, interpretability, and efficiency. Enter Modular Neural Networks (MNNs) — a powerful design paradigm that divides complex tasks into specialized, cooperative components. In this in-depth guide, we explore how modular architectures work, why they matter, and how you can implement them using PyTorch. From mixture-of-experts and dynamic routing to training strategies and real-world applications, this post walks through the full landscape of modular neural design—complete with mathematical insights, code examples, and a look at the future of scalable, flexible AI.

Our perception of color isn’t just about wavelength—it’s a dance between light level and vision. As brightness rises or falls, the very hues and saturations we believe to be constant subtly shift, revealing the dynamic interplay of photoreceptors, neural processing, and contextual adaptation.

In the digital age, our thoughts and beliefs are shaped by what we consume online. However, much of what we see is controlled by algorithms, which personalize search results, social media feeds, and recommendations based on our past behavior. This personalization creates an algorithmic bubble, reinforcing existing beliefs, limiting exposure to diverse viewpoints, and making it difficult to think independently.

Sustainable AI, also known as Eco-friendly AI or Green AI, refers to the development and use of artificial intelligence (AI) systems in a manner that minimizes their environmental impact and promotes long-term sustainability. This involves optimizing AI processes to reduce energy consumption, carbon emissions, and resource usage while ensuring ethical and responsible AI practices.

Structural Causal Models (SCMs) and Do-Calculus are foundational to causal reasoning and inference. SCMs formalize causal relationships through mathematical structures, while Do-Calculus provides tools to quantify and manipulate interventions in a causal system.

Delivering a package from destination "A" to destination "B" involves a series of processes that can be modeled mathematically using principles from logistics, optimization, and operations research.

The U.S. presidential election is a major event with significant implications for the sociological aspects. The policies and international relations stance of the U.S. can cause ripple effects worldwide due to its sociological impact.

The U.S. presidential election is a major event with significant implications for the global economy. The policies and international relations stance of the U.S. can cause ripple effects worldwide due to its economic influence and geopolitical power.

The Effect of the Ukraine-Russia War on the World Economy, Sociological Impacts, and Global Stock Markets

Women in India face numerous challenges across various sectors including the workplace, education, healthcare, and political representation.

In this post, I will talk about a method which I have designed to fine tune a human being to lower the expectation from LLM models.

This post will give examples of what bias I faced throughout different project I was involved.

This post perfroms a comparative study between multiple optimizers and how sensitive it behaves w.r.t. learning rate.

This post will simplify how time series decomposition work. This is not the exact breakdown of the package but it is a simplified version.

This post shows the paper which I have written in the year 2016. It is about searching a 2D array effective and fast.

This post shows us most possible ways to breakdown the timestamp and other features.

This links to the a package `brisque` developed for python. BRISQUE algorithm only gets the image whose quality you want to measure. This is thus called, No-Reference or Objective-Blind.

This post will visually help the readers to understand the curse of dimensionality for higher dimensional data.

Pairwise sequence alignment is a useful tool in many fields of biology. For example, the similarity between sequences can used be in evolutionary analysis to find out what organisms share a common ancestor.

Computer vision is an interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos. If we consider Digital images then it can be represented in different color space and models. The most common model us RBG but is many cases it fails to perform well over other models.

Here we will take an example of Simpson's Paradox and try to make a decision from the observed data and figure out the use of the role and importance of Causal Structure.

The Cellex qSARS-CoV-2 IgG/IgM Rapid Test is a lateral flow chromatographic immunoassay which can detect antibodies against th SARS-CoV-2 virus. We will answer a quation what is the probability of a person having an antibody when that is is tested positive for COVID.

Staring guide for Container Loading Problems. It covers the basic theoretical concepts to kickstart with this problem. The blog talks about the problem space, minimization problem, maximization problem and multiple constraints for the problems.

Updated with my experiements & insights about COVID-19/SARS-CoV-2

Is the trained model with 90% accuracy is not performing as expected in Production Environment?

An error that occurs when the computer attempts to handle a number that is too large for it. If during execution of Euclidean Distance it arrives at a number outside this range, How will you handle it ?

Deep Image Prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself.

Sampling is a fundamental aspect of statistics, but unlike the other methods of data collection, sampling involves choosing a method of sampling which further influences the data that you will result with. There are two major categories in sampling: probability and non-probability sampling.