Jerome Pasquero on Data Annotation: The Building Blocks of AI’s Future
Author(s): Louis-François Bouchard Originally published on Towards AI. How is Data Transforming AI — The What’s AI Podcast (episode 27) Good morning, fellow AI enthusiasts! It’s a fantastic day to dive into a newconversation that’s close to my heart. Joining methis week …
Understanding the Essence of Metrics in Machine Learning: Measuring the Success of Algorithms
Author(s): Vladimir Artus Originally published on Towards AI. Photo by Ricardo Arce on Unsplash Machine learning is a rapidly growing field that enables computers to automatically extract valuable data and make predictions based on them. But, how can we evaluate the quality …
Building a YoutubeGPT with LangChain, Gradio, and Vector Database
Author(s): Yanli Liu Originally published on Towards AI. Discovering the GenAI Development Stack through a Practical GuidePhoto by Tech Daily on Unsplash The world of Generative AI (GenAI) is evolving rapidly, making it easier and quicker than ever to develop AI-powered applications. …
AlphaGeometry: An Olympiad-level AI system for geometry
Author(s): Vishal Rajput Originally published on Towards AI. One of the signs of intelligence is being able to solve mathematical problems. And that is exactly what Google has achieved with its new Alpha Geometry System. And not some basic Maths problems, but …
GPT-5 is Coming: What We Know So Far & Its Release Date
Author(s): UPDF Originally published on Towards AI. GPT-4 is the world’s most widely used multimodal language model that has revolutionized AI. However, tech advancements simply have no limits. And that’s why the tech world is already looking for the next big thing, …
Guardrails for ML and DL Models: A Deep Dive into Regularization Techniques
Author(s): Shivamshinde Originally published on Towards AI. This article will explain the concept of regularization in ML and DL. The article will also introduce readers to some of the most famous regularization techniques. Photo by Bournes senruoB on Unsplash Table of Content …
Deploying and Using the Rerank Model
Author(s): zhaozhiming Originally published on Towards AI. In the Retrieval-Augmented Generation (RAG) process, the Rerank model plays a critical role. A typical RAG might retrieve a plethora of documents, not all of which are necessarily relevant to the query. Rerank steps in …
Clustering unveiled: The Intersection of Data Mining, Unsupervised Learning, and Machine Learning
Author(s): Anand Raj Originally published on Towards AI. Table of Contents Introduction K- Means Clustering Technique– Metrics for Clustering– K-means algorithm– Lloyd’s algorithm (approximation approach)– Initialization Sensitivity– K-means++ Initialization– Limitations of K-means– K-medoids with PAM algorithm– Determining the right K– Time and …
Prompt Engineering Guide for Open LLM: Take Your Open LLM Application to the Next Level
Author(s): Timothy Lim Originally published on Towards AI. Introduction: Why do we need another guide? Numerous prompt engineering guides have already been written. However, the majority of them focus on closed-source models characterized by their immense capacity, robust reasoning capabilities, and comprehensive …
Inside AlphaGeometry: Google DeepMind’s New Model that Solves Geometry Problems Like a Math Olympiad Gold-Medalist
Author(s): Jesus Rodriguez Originally published on Towards AI. Created Using DALL-E I recently started an AI-focused educational newsletter, that already has over 160,000 subscribers. TheSequence is a no-BS (meaning no hype, no news, etc) ML-oriented newsletter that takes 5 minutes to read. …
![Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 1]](https://i2.wp.com/miro.medium.com/v2/resize:fit:600/1*fMQPhi79ULwwPJnQHpK-Kg.png?w=600&resize=600,500&ssl=1 "Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 1]")
Prompt Engineering Best Practices for Instruction-Tuned LLM [Part 1]
Author(s): Youssef Hosni Originally published on Towards AI. Mastering Instruction-Tuned LLMs: Strategies for Effective Prompt Engineering Have you ever wondered why your interaction with a language model falls short of expectations? The answer may lie in the clarity of your instructions. Picture …
Understanding Why There Is No Such Thing as ‘Correct Probability’ in Data Science
Author(s): Peyman Kor Originally published on Towards AI. Probability Should be Conditioned on your Current State of Information Photo by Edge2Edge Media on Unsplash Intro In the field of data science, individuals frequently use the term “Probability”. However, a fundamental concept needs …
My Streamlit Love-Affair: Laser-Like GPT-4 Prompting For Interactive Data Visuals
Author(s): John Loewen, PhD Originally published on Towards AI. Clean prompts for error-free Python data visualization codeDall-E 2 image: impressionist painting of a combination of data visuals, including maps and charts How easy is it to create data visuals with GPT-4 and …
Understanding Sample Sizes: Let Them Do the Heavy Lifting
Author(s): Deepak Chopra | Talking Data Science Originally published on Towards AI. Top highlight This article is a continuation of the series on ‘Measuring Marketing Effectiveness’, in case you missed the previous parts, here is a quick recap: · Part 1: Themes: …
Phi-2, the New Year Gift for Language Model Lovers
Author(s): Kelvin Lu Originally published on Towards AI. Photo by Isabela Kronemberger on Unsplash After my utopian end-of-year holiday on a cruise without connection to the rest of the world, I can’t wait to return to my rational, real life. Microsoft's Phi-2, …
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