AI-Powered Portfolio Optimization: How LLMs Combine Quant + Narrative Data
Author(s): Suraj Pandey Originally published on Towards AI. How language and numeric signals together drive more robust portfolio strategies Imagine you’re managing a portfolio in early 2020. Traditional quantitative models show strong buy signals for airline stocks based on historical trends and …
5 Reasons Mom Entrepreneurs Stall on Rebrands (How AI Made Mine Possible)
Author(s): Iryne Vanessa Somera Originally published on Towards AI. Why hesitation holds me back and how AI clears the path I am a mom and a business owner. My days are full, starting with client work, school prep, and meals. When I …
Deep Dive into Modern Natural Language Processing
Author(s): Sunil Rao Originally published on Towards AI. NLP models have quietly shaped your digital world, from virtual assistants to search results, and their evolution is accelerating. Read this article for a clear overview of the NLP, architectures — from RNN to …
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 …
Fine-Tuning and Aligning Large Language Models: A Guide to SFT, RLHF, and What Comes Next
Author(s): M Originally published on Towards AI. A beginner-friendly guide. If you've used ChatGPT, Claude, or any other modern AI assistant, you've used a model that has undergone a complex training process. These models not only learn from large amounts of text …
LAI #96: From Building LLMs by Hand to Smarter Agent Patterns
Author(s): Towards AI Editorial Team Originally published on Towards AI. Good morning, AI enthusiasts! AI isn’t just about bigger models; it’s about building smarter, more trustworthy systems. This week, we start with the fundamentals: a step-by-step guide to creating an LLM from …
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 …
Human-in-the-Loop (HITL) in AutoGen — Deep Dive Part 3
Author(s): Aayushi_Sharma Originally published on Towards AI. Human-in-the-Loop (HITL) in AutoGen — Deep Dive Part 3 Imagine this: you’ve built a powerful team of AI agents — researchers, analysts, planners — working in sync. But instead of just watching them go on autopilot, …
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 …
Build Your Own AI Sidekick with Claude Agents SDK (Beginner-Friendly Guide)
Author(s): Nishad Ahamed Originally published on Towards AI. Ever wished your editor could build features from notes, fix bugs, surf unfamiliar repos, and even spin up a web server — while you sip coffee? Meet Claude Agents SDK + Claude Code. Together, …
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