GPU Architecture & Working intuitively explained
Author(s): Allohvk Originally published on Towards AI. GPU Origins The image displayed on a computer screen is made up of millions of tiny pixels. In early days, “graphics controllers” were given instructions by the CPU on how to calculate the individual pixel …
Language models are transfer learners: using BERT to solve Multi-Hop RAG
Author(s): Anuar Sharafudinov Originally published on Towards AI. Credits: GPT4.1 Introduction In previous article, we addressed a critical limitation of today’s Retrieval-Augmented Generation (RAG) systems: missing contextual information due to independent chunking. However, this is just one of RAG’s shortcomings. Another significant …
The Evolution of GRPO: DAPO
Author(s): tangbasky Originally published on Towards AI. Dynamic sAmpling Policy Optimization (DAPO) is actually a type of reinforcement learning optimization algorithm. To thoroughly understand DAPO, we need to progressively sort out and explain it from PPO -> GRPO -> DAPO. Proximal Policy …
Retaining Knowledge in AI: Solving Catastrophic Forgetting in LLMs
Author(s): Sanket Rajaram Originally published on Towards AI. Part 1: The Learning Journey of a Kid in the School Imagine a kid in school learning about basic arithmetic in one semester. By the next year, they move on to geometry and algebra, …
The Generative AI Model Map
Author(s): Ayo Akinkugbe Originally published on Towards AI. Photo by Jackson Simmer on Unsplash Introduction With the commercialization of the GPT model in 2022, generative AI (artificial intelligence) became popular. However large language models — the category of generative models GPT belongs …
Why Mutual Information Deserves More Love Than It Gets
Author(s): Ashwin Biju Alikkal Originally published on Towards AI. Correlation brought a ruler to a curve fight. MI brought the truth. ✨ Introduction A few months into working on ML projects, I was pretty confident in my ability to identify useful features. …
Log Link vs Log Transformation in R — The difference that misleads your entire data analysis
Author(s): Ngoc Doan Originally published on Towards AI. Image by Unsplash Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it's tempting for us to utilize log transformations …
Supervised vs Unsupervised Learning | The First Big Choice in ML | M003
Author(s): Mehul Ligade Originally published on Towards AI. Supervised vs Unsupervised Learning | The First Big Choice in ML | M003 📘 Contents Why This Article Matters What Learning Really Means in Machine Learning The Two Major Branches of ML Supervised Learning: …
What Models Prefer to Learn: A Geometric Framing of Architecture and Regularization
Author(s): Sigurd Roll Solberg Originally published on Towards AI. An adventure through unknown landscapes. By Grok. Intro What does a Neural Network really learn? Every machine learning model, deep or shallow, learns by searching within a “hypothesis space” — the set of …
Mastering AI Agents: Components, Frameworks, and RAG
Author(s): Sunil Rao Originally published on Towards AI. Credit: Shutterstock Agents are advanced AI systems that use LLMs as their core “brain” to interpret language, reason through problems, plan solutions, and autonomously execute tasks — often by interacting with external tools or …
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