How to Easily Fine-Tune the Donut Model for Receipt Information Extraction
Author(s): Eivind Kjosbakken Originally published on Towards AI. How to Easily Fine-Tune the Donut Model for Receipt Information Extraction The Donut model in Python is a model to extract text from a given image. This can be useful in several scenarios, for …
Learn SARSA the Easy Way: Your First Temporal Difference Algorithm
Author(s): Rem E Originally published on Towards AI. Tutorial 9.1: Implementing the SARSA Algorithm for Our Maze Problem Now we’re ready to start implementing our first Temporal Difference (TD) method: SARSA! This tutorial builds on Tutorial 8.2, so make sure to check …
AI Agent Revolution: How Anthropic Cut Token Usage by 98% with Code Execution
Author(s): AbhinayaPinreddy Originally published on Towards AI. The Problem That’s Been Quietly Killing AI Agents Imagine hiring an assistant who needs to read a 500-page instruction manual before making a simple phone call. That’s essentially what’s been happening with AI agents — …
Build a Blazing-Fast YouTube Summarizer: Groq, LangChain, and Streamlit
Author(s): A.Venkatesh Originally published on Towards AI. Introduction In the age of infinite content, who has time to watch a 45-minute technical video to find a single piece of information? We need instant knowledge. Youtube Video Summarizer with translation, notes and recommendationsThis …
The AI Revolution Nobody Saw Coming: How DeepSeek OCR Just Made Your Documents 10x Cheaper 🚀
Author(s): MahendraMedapati Originally published on Towards AI. The AI Revolution Nobody Saw Coming: How DeepSeek OCR Just Made Your Documents 10x Cheaper 🚀 Remember when we thought scanning documents was solved? Yeah… about that. Image description not provided in the original content.The …
Why Your AI Agent Will Fail Without Human Oversight
Author(s): Sai Kumar Yava Originally published on Towards AI. Human in the Loop : Building Reliable AI Picture this: Your company deploys an AI agent to handle customer support tickets. For weeks, it works beautifully — until one day it confidently tells …
Choosing the right GenAI customization strategy: Balancing cost, control, and performance
Author(s): Laura Verghote Originally published on Towards AI. A practical framework to choose between RAG, fine-tuning, continued pre-training, and training from scratch — through the lens of balancing cost, control, performance and compliance. As Generative AI systems move from prototypes to production, …
RoPE (Rotary Position Embeddings): A Detailed Example
Author(s): Utkarsh Mittal Originally published on Towards AI. In transformer models, knowing the order of tokens is essential — even though the model processes tokens in parallel. Traditional positional embeddings rely on a fixed “lookup table” (learned for positions up to a …
From JSON to TOON: Evolving Serialization for LLMs
Author(s): Kushal Banda Originally published on Towards AI. TOON (Token Oriented Object Notation) When you’re scaling AI applications, token efficiency isn’t just a buzzword it’s your bottom line. Every token wasted is money left on the table and latency you didn’t ask …
Data Lakes in Enterprises
Author(s): Flora Nanda Originally published on Towards AI. Data is now widely seen as the new “gold standard” in the AI revolution. In the context of AI, data is the critical foundation and enabler for everything from model training to real-time decision-making …
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