AI

NeRF overview — Image by Author A basic understanding of NeRF’s workings through visual representations Aqeel Anwar · Follow Published in Towards Data Science · 10 min read · 3 days ago — Who should read this article? This article aims to provide a basic beginner level understanding of NeRF’s workings through visual representations. While various blogs offer detailed explanations of NeRF, these are often geared toward readers with a strong technical background in volume

GANs and SMOTE explained through bartending: Data Science for Machine Learning Series (1) Sunghyun Ahn · Follow Published in Towards Data Science · 12 min read · 1 day ago — SMOTE Technique Mechanism If you are not a paid member on Medium, I make my stories available for free: Friends link Personally, it feels like data is treated as the new oil of the digital age. This feels especially true with the boom in

STATISTICS A look into the strange first digit distribution of naturally occurring datasets Maria Mouschoutzi, PhD · Follow Published in Towards Data Science · 9 min read · 8 hours ago — Image created by the author using GPT-4 / All other images created by the author unless specified otherwise Ever heard a co-worker confidently declaring something like “The longer I lose at roulette, the closer I am to winning?” Or had a boss that

Practical insights for a data-driven approach to model optimization David Martin · Follow Published in Towards Data Science · 11 min read · 3 days ago — Photo by Joshua Sortino on Unsplash It is said that in order for a machine learning model to be successful, you need to have good data. While this is true (and pretty much obvious), it is extremely difficult to define, build, and sustain good data. Let me share

A deep dive into the building block of quantum computers. Sara A. Metwalli · Follow Published in Towards Data Science · 10 min read · 9 hours ago — Photo by Google DeepMind on Unsplash In honor of the International Year of Quantum Technology, I plan to write as many articles as possible about different aspects of the quantum field. However, to discuss deeper and more technical topics, I first need to explain the basics

Here’s how to optimize the delivery routes, from theory to code. Piero Paialunga · Follow Published in Towards Data Science · 11 min read · 6 hours ago — Photo by Mudit Agarwal on Unsplash The code of this article can be found on this GitHub folder. One of my favorite professors throughout my studies told me this: “Just because your algorithm is inefficient, it doesn’t mean that the problem is hard” This means that

Lag columns can significantly boost your model’s performance. Here’s how you can use them to your advantage Haden Pelletier · Follow Published in Towards Data Science · 7 min read · 7 hours ago — Image by author The nature of a time series model is such that past values often affect future values. When there’s any kind of seasonality in your data (in other words, your data follows an hourly, daily, weekly, monthly or

Examining the expressive capacity of Graph Attention Networks Hunjae Timothy Lee · Follow Published in Towards Data Science · 7 min read · 4 days ago — Image by the author In graph representation learning, neighborhood aggregation is one of the most well-studied and investigated areas, among which attention-based methods largely remain state-of-the-art. Leveraging learnable attention scores for weighted aggregations, graph attention networks exhibit higher expressivity than naive aggregation schemes. In graph attention, the most

Ever wondered why the time to first token in LLMs is high but subsequent tokens are super fast? Rohit Ramaprasad · Follow Published in Towards Data Science · 11 min read · 3 days ago — In this post, I dive into the details of KV-Caching used in Mistral, a topic I initially found quite daunting. However, as I delved deeper, it became a fascinating subject, especially when it explained why the time to first

Decide what to do with experiment results in code Zach Flynn · Follow Published in Towards Data Science · 5 min read · 8 hours ago — Photo by Cytonn Photography on Unsplash The experiment lifecycle is like the human lifecycle. First, a person or idea is born, then it develops, then it is tested, then its test ends, and then the Gods (or Product Managers) decide its worth. But a lot of things happen