Top 20 LLM Interview Questions
Author(s): Ahmed Boulahia Originally published on Towards AI. Essential questions and clear answers to help you prepare with confidence. So, are you looking for a job as an AI engineer, data scientist, machine learning engineer, or even a data engineer? Or maybe …
Bridging Symbolic AI and Deep Learning: How Knowledge Graphs are Revolutionizing ResNets
Author(s): Jitesh Prasad Gurav Originally published on Towards AI. When ResNet revolutionized computer vision in 2015, it solved the vanishing gradient problem that plagued deep neural networks. Today, a new revolution is underway: researchers are discovering that by infusing ResNets with structured …
Can AI Learn by Repeating Itself?
Author(s): Arthur Lagacherie Originally published on Towards AI. Recursion could reshape how LLMs scale. A major problem with current LLM architectures is the difficulty of adapting their computational power to match the performance requirements of specific tasks (low performance requirements should use …
Building Multi-Agent Teams with AutoGen: Deep Dive Part 2
Author(s): Aayushi_Sharma Originally published on Towards AI. 🧠 What happens when a single AI agent isn’t enough to solve a problem? In the first part of our AutoGen series, we explored the foundations of this powerful multi-agent framework — its architecture, agent …
A Simple (But Not Too Simple) Intro to Linear Estimators
Author(s): Maxwell’s Demon Originally published on Towards AI. Optimally combining prior knowledge with new data Let’s start with an example. Say a lab technician knows from long-term experience that the lab’s temperature usually hovers around 20 °C. On a particular day, she …
Can Recursion Make LLMs Smarter and More Efficient?
Author(s): Arthur Lagacherie Originally published on Towards AI. Recursion could reshape how LLMs scale. A major problem with current LLM architectures is the difficulty of adapting their computational power to match the performance requirements of specific tasks (low performance requirements should use …
Jet-Nemotron: NVIDIA’s New AI Architecture Achieves 53x Speed Improvement
Author(s): MKWriteshere Originally published on Towards AI. How the PostNAS framework delivers faster language model inference without sacrificing accuracy across benchmarks Large language models consume massive computational resources. Your company’s AI bills keep climbing. Processing times frustrate users waiting for responses. Image …
Beginner’s Visual Guide to Quantisation Methods for LLMs
Author(s): Parth Chokhra Originally published on Towards AI. A Visual Step-by-Step Guide to Popular Quantisation Techniques Quantisation is the process of reducing the precision of numbers used in a model; for example, storing weights in 8-bit integers instead of 16- or 32-bit …
Comparing Four Time Series Forecasting Methods: Prophet, DeepAR, TFP-STS, and Adaptive AR
Author(s): Shenggang Li Originally published on Towards AI. A practical evaluation of models from Meta, Amazon, Google, and a new adaptive AR approach Time series forecasting is everywhere — in business, finance, retail, and even public policy. The challenge is simple to …
Risk-Adjusted Returns with Python (Part 1): The Treynor Ratio
Author(s): Siddharth Mahato Originally published on Towards AI. “Risk comes from not knowing what you’re doing.” — Warren Buffett Most investors chase returns. But ask any seasoned fund manager, and you’ll hear a different question:“Am I being rewarded fairly for the risks …
How REFRAG Delivers 30× Faster RAG Performance in Production
Author(s): MKWriteshere Originally published on Towards AI. Intelligent context compression reduces latency and infrastructure costs for development teams If you’ve ever built a Retrieval-Augmented Generation system, you know the pain. Your chatbot pulls 20 relevant documents, feeds them to your LLM, and …
From Manual EDA to AI-Powered Agents: A Hands-On Experiment with LangChain
Author(s): Sarah Lea Originally published on Towards AI. Can an agent take over repetitive EDA tasks? A quick LangChain experiment. Exploratory data analysis (EDA) is a standard step before we train models or make predictions. Own visualization — Illustrations from unDraw.coThe article …
Detailed Guide to Quantisation Methods for LLMs
Author(s): Parth Chokhra Originally published on Towards AI. A Visual Step-by-Step Guide to Popular Quantisation Techniques Quantisation is the process of reducing the precision of numbers used in a model; for example, storing weights in 8-bit integers instead of 16- or 32-bit …
5 ML Mistakes That Scream “Student” (And How to Fix Them) 🚀
Author(s): MahendraMedapati Originally published on Towards AI. From campus to career-ready: Transform your machine learning projects with these industry insights As a student diving deep into machine learning, you’ve probably built some cool projects, aced those assignments, and maybe even topped a …
Introduction to RAG: Basics to Mastery. 5-Advanced RAG: Fast Retrieval (ANN) and Reranking
Author(s): Taha Azizi Originally published on Towards AI. Part 5 of the mini-series introduction to RAG Introduction In the earlier articles, we built RAG pipelines that worked great for small datasets.But what happens when your knowledge base grows to millions or billions …
Recent Posts
