Ollama vs. The Giants: Can Your Laptop Really Run a 671B Model?
Author(s): R. Thompson (PhD) Originally published on Towards AI. …The surprising truth about cloud-powered AI that feels local Picture a young developer, Maya, sitting in a cramped apartment with a modest laptop. She dreams of testing DeepSeek-V3.1, a model so vast it …
Cracking Q-Learning
Author(s): Rem E Originally published on Towards AI. Mastering the second key method in Temporal Difference learning Last time, we learned the concept of Temporal Difference (TD) learning and explored our first method: SARSA (On-Policy).This time, we’ll dive into the Off-Policy TD …
Do AI Agents Really Use the Tools You Build for Them? I Tested It.
Author(s): Marie Humbert-Droz, PhD Originally published on Towards AI. Testing tool coverage in local agents and how to improve compliance. I thought my healthcare AI agent would call my lab-checking tool every time it encountered lab values. Instead? Only 1 out of …
Understanding Neural Networks — and Building One!
Author(s): Aditya Gupta Originally published on Towards AI. Why Do We Need Neural Networks? Imagine trying to teach a computer to do something humans find easy like recognizing a face in a photo, understanding someone’s accent, or predicting which movie you’ll enjoy …
The GPU Bottleneck: Why Your Multi-GPU Training is Crawling (and How to Fix It!) 🚀 | GPU 瓶頸：為什麼你的多 GPU 訓練比你想像的還要慢（以及如何解決！）
Author(s): ChalBe Originally published on Towards AI. The GPU Bottleneck: Why Your Multi-GPU Training is Crawling (and How to Fix It!) 🚀 | GPU 瓶頸：為什麼你的多 GPU 訓練比你想像的還要慢（以及如何解決！） tags: Pytorch | DistributedDataParalle(DDP) | Performance Optimization So, you’ve assembled a beast of machine with …
DBSCAN Clustering Demystified: A Visual Walkthrough
Author(s): Niraj Originally published on Towards AI. If you’ve ever tried to cluster data with varying densities or irregular shapes, you’ve likely discovered that traditional algorithms like K-Means fall short. In my previous article, Beyond Accuracy: A Guide to Classification Metrics, we …
LLMs Don’t Just Need to Be Smart — They Need to Be Specific. Here’s How.
Author(s): Kaushik Rajan Originally published on Towards AI. How a new technique called “Test-Time Deliberation” teaches AI to think before it speaks I spend a lot of my time wrestling with Large Language Models (LLMs). The goal is always the same: how …
Beyond “Looks Good to Me”: How to Quantify LLM Performance with Google’s GenAI Evaluation Service
Author(s): Jasleen Originally published on Towards AI. The Production Hurdle The greatest challenge faced by industry today is converting a solution from demo to production. And the main reason behind this is confidence in the results. The evaluation dataset and metrics that …
Understanding LLM Sampling: Top-K, Top-P, and Temperature
Author(s): Sai Bhargav Rallapalli Originally published on Towards AI. Mastering Creativity and Control with Temperature, Top-K, and Top-P LLM sampling is how a model decides the next word to generate from a list of possibilities. Rather than simply picking the most likely …
Beyond pre-trained LLMs: Augmenting LLMs through vector databases to create a chatbot on organizational data
Author(s): Leapfrog Technology Originally published on Towards AI. In the ever-evolving realm of AI-driven applications, the power of Large Language Models (LLMs) like OpenAI’s GPT and Meta’s Llama2 cannot be overstated. In our previous article, we introduced you to the fascinating world …
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