ML Fundamentals

What I learned building a political bias classifier — and why the most interesting result wasn’t the best one. Continue reading on Medium »

A technical deep dive into AutoEncoder anomaly scoring, Gradient Boosting ensembles, SHAP explainability, and real-time Kafka streaming. Continue reading on Med

A technical deep dive into AutoEncoder anomaly scoring, Gradient Boosting ensembles, SHAP explainability, and real-time Kafka streaming. Continue reading on Med

A technical deep dive into AutoEncoder anomaly scoring, Gradient Boosting ensembles, SHAP explainability, and real-time Kafka streaming. Continue reading on Med

As developers, we are always looking for interesting datasets to test our machine learning skills....

If you’re a developer stepping into AI and ML for the first time, the terminology can feel like a wall. Loss functions, transformers… Continue reading on Medium

Most multi-step AI pipelines are built around a simple question: did this output pass or fail? Each step gets a verdict. If it passes, the… Continue reading on

Most trading strategies don’t fail because they’re wrong. Continue reading on Medium »

In January 2026, the Bombay Stock Exchange had to issue an emergency public warning. Deepfake videos of their CEO were circulating online… Continue reading on M

Part 4 — https://medium.com/@mittalutkarsh/the-training-pipeline-with-one-row-flowing-through-every-stage-part4-2797aa2e6c2d Continue reading on Medium »

🌿 Introduction Agriculture plays a vital role in our daily life. Farmers often face...

Building with Data | Part 6: The Series Finale Continue reading on Medium »

Building with Data | Part 6: The Series Finale Continue reading on Medium »

LeetCode 74: Search a 2D Matrix - Conquer the Grid with Binary Search! Hey fellow coders...

What works, what doesn’t, and how to make them work beyond the hype Continue reading on Data Science Collective »

What works, what doesn’t, and how to make them work beyond the hype Continue reading on Data Science Collective »

Background noise is everywhere — fans, traffic, keyboard clicks, chatter. Yet tools like Microsoft Teams make voices sound clean in real… Continue reading on Me

Recap: From RNN to LSTM Continue reading on Medium »

Recap: From RNN to LSTM Continue reading on Medium »

Recap: From RNNs to the Need for Better Memory Continue reading on Medium »

Recap: From RNNs to the Need for Better Memory Continue reading on Medium »

Artificial Intelligence (AI) is no longer a futuristic concept—it’s part of our everyday lives, from search engines to virtual assistants. If you’ve ever wonder

Dalam analisis data teks (Natural Language Processing / NLP), salah satu tantangan terbesar adalah menemukan inti pembicaraan atau kata… Continue reading on Med

Training a model is easy. Getting 85–90% accuracy in a notebook? Also doable. But getting that model...

Unsupervised machine learning is a branch of machine learning where models are trained on data...

When AI training data becomes a liability instead of an asset Continue reading on Medium »

Learning data structures and algorithms (DSA) is essential for anyone interested in computer science, software engineering, or programming… Continue reading on

Learning data structures and algorithms (DSA) is essential for anyone interested in computer science, software engineering, or programming… Continue reading on

Learning data structures and algorithms (DSA) is essential for anyone interested in computer science, software engineering, or programming… Continue reading on

5 Critical AI Predictive Maintenance Pitfalls and How to Avoid Them Every failed AI project has a story. The predictive maintenance pilot that identified hundre

AI Predictive Maintenance Approaches: Comparing Methods and Tools Choosing the right approach for predictive maintenance can feel like navigating a maze of comp

Categorical data is commonly organized by human beings or machine learning models. Which approach to take and how decisions are made in… Continue reading on Med

Categorical data is commonly organized by human beings or machine learning models. Which approach to take and how decisions are made in… Continue reading on Med

Why your tests might be lying to you and how to catch them I've seen projects with 90%...

Introduction Continue reading on Medium »

arXiv:2604.26211v1 Announce Type: new Abstract: In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning b

arXiv:2604.26237v1 Announce Type: new Abstract: This study applied the Apriori algorithm to analyze behavioral interaction patterns associated with learned help

arXiv:2604.26644v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on mathematical reasoning tasks but remain unreliable o

arXiv:2604.25943v1 Announce Type: cross Abstract: Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineerin

arXiv:2604.25945v1 Announce Type: cross Abstract: Wireless radiance field (WRF) reconstruction aims to learn a continuous, queryable representation of radio fre

arXiv:2604.25978v1 Announce Type: cross Abstract: Prior work on node classification has shown that Graph Neural Networks (GNNs) can learn representations that t

arXiv:2604.26024v1 Announce Type: cross Abstract: Class-level evaluation can conceal substantial performance disparities across subconcepts within the same clas

arXiv:2604.26039v1 Announce Type: cross Abstract: The optimal kernel configuration for Mixture-of-Experts (MoE) inference depends on both batch size and the exp

arXiv:2604.26073v1 Announce Type: cross Abstract: Industrial chemical plants often operate under strict data confidentiality constraints, making centralized dat

The first thing I built at NovumAI wasn’t the model. It was the eval harness. Continue reading on Medium »

A rebuilt pitch-quality stack with sharper predictiveness, faster stabilization, and a cleaner split between what a pitch is and what a… Continue reading on Med

A rebuilt pitch-quality stack with sharper predictiveness, faster stabilization, and a cleaner split between what a pitch is and what a… Continue reading on Med

The most dangerous ML metric is one that looks right — and this is exactly what that failure looks like in production. Continue reading on Medium »