April 19, 2024

AI

Practical Computer Simulations for Product Analysts

Part 1: Task-specific approaches for scenario forecasting Mariya Mansurova · Follow Published in Towards Data Science · 20 min read · 3 hours ago — Image by DALL-E In product analytics, we quite often get “what-if” questions. Our teams are constantly inventing different ways to improve the product and want to understand how it can affect our KPI or other metrics. Let’s look at some examples: Imagine we’re in the fintech industry and facing new

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AI

How to Implement Knowledge Graphs and Large Language Models (LLMs) together at the Enterprise Level

Source: OpenArt SDXL A survey of the current methods of integration Steve Hedden · Follow Published in Towards Data Science · 13 min read · 3 hours ago — Large Language Models (LLMs) and Knowledge Graphs (KGs) are different ways of providing more people access to data. KGs use semantics to connect datasets via their meaning i.e. the entities they are representing. LLMs use vectors and deep neural networks to predict natural language. They are

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AI

The Business Guide to Tailoring Language AI Part 2

Prompting ChatGPT and other chat-based language AI — and why you should (not) care about it Georg Ruile, Ph.D. · Follow Published in Towards Data Science · 12 min read · 3 hours ago — Foreword This article sheds some light on the question of how to “talk” to Large Language Models (LLM) that are designed to interact in conversational ways, like ChatGPT, Claude and others, so that the answers you get from them are

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AI

Pandas: My Experience Contributing to a Major Open Source Project

Open Source It just might be worth you contributing too Mike Clayton · Follow Published in Towards Data Science · 18 min read · 3 hours ago — Photo by Markus Winkler on Pexels Open source projects generally rely on the contributions of a multitude of people to keep them bug-free, secure, up to date, and constantly moving forward. Who are these people? Well, “they” could be you and me! There is nothing stopping us

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AI

Information Rationalization in Large Organizations

How can we use clustering techniques to combine and refactor a large number of disparate dashboards? Ramkumar K · Follow Published in Towards Data Science · 11 min read · 1 day ago — Photo by Luke Chesser on Unsplash Background Organizations generate voluminous amounts of data on a daily basis. Dashboards are built to analyze this data and derive meaningful business insights as well as to track KPIs. Over time, we find ourselves with

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Evaluation Our Approach on ARC and Beyond: A Look Back at Our Experiments | HackerNoon

Too Long; Didn’t Read In this section, we first evaluate our approach on ARC, comparing it to existing approaches in terms of success rates, efficiency, model complexity, and model naturalness. We then evaluate the generality of our approach beyond ARC by applying it to a different domain, spreadsheets, where inputs and outputs are rows of strings. Our experiments were run with single-thread implementations on Fedora 32, Intel Core i7x12 with 16GB memory. We used one

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Checklist for FinTech Product Growth: Channels, Trends, and Tools | HackerNoon

Too Long; Didn’t Read This article outlines the key metrics at different stages of the user journey, from acquisition to retention. It emphasizes the importance of crafting an effective marketing mix, optimizing the onboarding process, and enhancing retention by improving the metrics detailed in the article, utilizing the instruments and strategies provided.

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