RLAD: How AI Learns to Think Strategically Before Solving Hard Problems
Author(s): MKWriteshere Originally published on Towards AI. A new training method teaches language models to generate reasoning strategies first, improving accuracy by 44% on complex math problems Large language models struggle with a specific problem: they optimize for generating longer solutions instead …
Building and Deploying a RAG Application: From PDF Processing to Production
Author(s): Ashutosh Malgaonkar Originally published on Towards AI. Overview I built a Retrieval-Augmented Generation (RAG) system that answers physics questions by retrieving relevant passages from an AP Physics textbook and generating responses using an LLM. The application processes 500 pages of Electricity …
Data Normalization in ML
Author(s): Amna Sabahat Originally published on Towards AI. In the realm of machine learning, data preprocessing is not just a preliminary step; it’s the foundation upon which successful models are built. Among all preprocessing techniques, normalization stands out as one of the …
The RAG Playbook: A Data Science Guide to Document Chunking
Author(s): The Bot Group Originally published on Towards AI. The RAG Playbook: A Data Science Guide to Document Chunking Large Language Models are only as smart as the data we feed them. This is the fundamental challenge at the heart of modern …
How to Surgically Edit LLMs Without Retraining in Data Science
Author(s): The Bot Group Originally published on Towards AI. How to Surgically Edit LLMs Without Retraining in Data Science Your large language model is a marvel of engineering, trained on vast datasets at an enormous cost. It’s powerful, fluent, and… wrong. It …
The Model That Broke All the Rules in Data Science
Author(s): The Bot Group Originally published on Towards AI. The Model That Broke All the Rules in Data Science For years, the world of sequence modeling was dominated by a single, stubborn idea: to understand language, a model had to process it …
The DINOv3 Playbook for Computer Vision Data Science
Author(s): The Bot Group Originally published on Towards AI. The DINOv3 Playbook for Computer Vision Data Science Self-supervised learning (SSL) has long been the holy grail of machine learning. The promise is simple yet transformative: train powerful foundation models on massive, unlabeled …
Your Model Has 95% Accuracy. It’s Completely Useless.
Author(s): Rohan Mistry Originally published on Towards AI. You Built a Model That Predicts Everything as “No.” It Has 95% Accuracy. You Just Shipped Garbage. Your boss: “How’s the fraud detection model?”You: “95% accuracy! Ready to deploy!”Your boss: “Great! Ship it.” Source: …
Data Leakage: Your 99% Accuracy Model is a Lie
Author(s): Rohan Mistry Originally published on Towards AI. Training Accuracy: 99%. Production Accuracy: 53%. Welcome to Data Leakage Hell. You spent 3 months building the perfect model. Source: Image by author.The article discusses the challenges of data leakage in machine learning, where …
The ML Algorithm Selector: When to Use Which Machine Learning Algorithm
Author(s): Rohan Mistry Originally published on Towards AI. You Know How Every Algorithm Works. But You Have No Idea Which One to Actually Use. You aced your ML course. You know Random Forest, XGBoost, SVM, Neural Networks. Source: Image by Author.This article …
LLMs Don’t Think. They Just Get Lucky.
Author(s): Devrim Ozcay Originally published on Towards AI. LLMs Don’t Think. They Just Get Lucky. I spent six months feeding GPT-4 the same prompt 10,000 times and got 10,000 different answers. Not slightly different. Completely different. Same temperature. Same parameters. Same everything. …
Unboxing AI: The Data Science of True Model Interpretability
Author(s): The Bot Group Originally published on Towards AI. Unboxing AI: The Data Science of True Model Interpretability For years, the promise of artificial intelligence has been shadowed by a fundamental problem: the black box. We build powerful models that achieve incredible …
AI Meets Personal Finance: Building a Smart Expense Analyzer with LangGraph
Author(s): Vikram Bhat Originally published on Towards AI. Introduction Managing personal finances shouldn’t feel like a second job. Yet most people struggle with basic questions: Where is my money actually going? Am I paying for subscriptions I forgot about? Why was this …
Benchmarking LLMs for PhD-Level Science Problems
Author(s): Nicholas Poon Originally published on Towards AI. Curie A ground-breaking benchmark by Google, Harvard, Cornell University, NIST, and other institutions. As always, read this article for free here if you’re not a member.The article discusses the Curie benchmarking framework, which aims …
How Hypothesis Testing is Actually Used in Machine Learning
Author(s): Nikhil Dasari Originally published on Towards AI. A simple walkthrough of how and where we use hypothesis testing in real ML workflows. When I first started learning Machine Learning, I was confused about how hypothesis testing is used. Many of you …
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