The Roadmap of Mathematics for Machine Learning
Author(s): Tivadar Danka Originally published on Towards AI. A complete guide to linear algebra, calculus, and probability theory Understanding the mathematics behind machine learning algorithms is a superpower. Here’s the full roadmap for you.This article presents a comprehensive curriculum that guides readers …
3 Game-Changing Tools for Modern Data Science
Author(s): Mohamed Abdelsalam Originally published on Towards AI. Introduction The rise of LLMs facilitates “vibe coding,” making it fast to generate initial Python scripts. However, this ease creates a false sense of progress. Building professional-grade data products requires more than quick scripts; …
I “Vibe Coded” Using Cursor (No Code Required)
Author(s): Adi Insights and Innovations Originally published on Towards AI. I “Vibe Coded” Using Cursor (No Code Required) Andrej Karpathy’s new philosophy changes everything. Here is how I built a RAG app by just talking to my computer. Here is how I …
7 Powerful Prompt Engineering Techniques That Transform LLM Performance
Author(s): TANVEER MUSTAFA Originally published on Towards AI. 7 Powerful Prompt Engineering Techniques That Transform LLM Performance Prompt engineering is the critical skill of crafting instructions that guide Large Language Models (LLMs) to produce reliable, structured outputs. This article explores how the …
Transformers v5 – Hugging Face’s Next Big Leap in Simple and Powerful AI Models
Author(s): Aniket Sanyal Originally published on Towards AI. Transformers v5 – Hugging Face’s Next Big Leap in Simple and Powerful AI Models Hugging Face has unveiled Transformers v5, the latest major release of its popular open-source library that powers many AI models …
The 4 Parameter-Efficient Fine-Tuning Methods: How to Adapt LLMs 100× Faster
Author(s): TANVEER MUSTAFA Originally published on Towards AI. The 4 Parameter-Efficient Fine-Tuning Methods: How to Adapt LLMs 100× Faster You want to customize GPT-3 for customer service. Traditional fine-tuning requires updating 175 billion parameters — 350GB storage per variant, weeks of training, …
The 4 RAG Architectures: How to Give AI Perfect Memory Without Retraining
Author(s): TANVEER MUSTAFA Originally published on Towards AI. Understanding Naive RAG, Advanced RAG, Modular RAG, and Agentic RAG Your LLM is brilliant but frustratingly limited. Image generated by Author using AIThis article delves into the concept of Retrieval Augmented Generation (RAG), discussing …
We Replaced 47 Excel Files With One Power BI Model. Here’s What Actually Happened.
Author(s): Gulab Chand Tejwani Originally published on Towards AI. 15 hours every Monday copying data. Daily errors. Zero trust in the numbers. Here’s what actually happened when we migrated from Excel chaos to Power BI. Monday, 6:23 AM. My phone buzzed. We …
9 Agentic AI Projects I’d Build in 2026 to Learn What Agents Really Are
Author(s): Khushbu Shah Originally published on Towards AI. Most “ AI agent demos” online are just chatbots with a loop. I’ve seen enough agent demos that look impressive and teach nothing. These AI agent projects are different. Each one forces you to …
The 5 Normalization Techniques: Why Standardizing Activations Transforms Deep Learning
Author(s): TANVEER MUSTAFA Originally published on Towards AI. The 5 Normalization Techniques: Why Standardizing Activations Transforms Deep Learning Training deep neural networks is difficult. Add more layers, and training becomes unstable — gradients explode or vanish, learning slows, or the model fails …
How We Built a 99% Accurate Invoice Processing System Using OCR and LLMs
Author(s): Vaibhav Rathi Originally published on Towards AI. We had a working RAG solution at 91% accuracy. Here’s why we rebuilt it with fine-tuning and what we learned along the way. Our client was spending eight minutes per invoice on manual data …
When Optimization Works: The Role of Convexity in Business Decisions
Author(s): Saif Ali Kheraj Originally published on Towards AI. Every business decision operates under constraints, budgets, capacity, regulations, and trade-offs. The structure of those constraints determines whether a decision has a single clear optimal choice or several competing alternatives. Convex problems lead …
Mastering Unstructured data: The Blueprint For Efficient Solution
Author(s): Pankaj Agrawal Originally published on Towards AI. In the rapidly evolving landscape of Artificial Intelligence, the spotlight has shifted from neatly organized tables to the vast, messy, and context-rich world of unstructured data., Comprising the vast majority of enterprise information, formats …
Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction | Towards Data Science
Author(s): Marco Hening Tallarico Originally published on Towards AI. Bonferroni vs. Benjamini-Hochberg: Choosing Your P-Value Correction | Towards Data Science P-values can be a sensitive topic. Perhaps best avoided on first encounter with a Statistician. The disposition toward the topic has led …
The 4 Flash Attention Variants: How to Train Transformers 10× Longer Without Running Out of Memory
Author(s): TANVEER MUSTAFA Originally published on Towards AI. The 4 Flash Attention Variants: How to Train Transformers 10× Longer Without Running Out of Memory You’re training a Transformer. Image generated by Author using AIThis article discusses four Flash Attention variants that enhance …
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