November 24, 2024

AI

Perform outlier detection more effectively using subsets of features

Identify relevant subspaces: subsets of features that allow you to most effectively perform outlier detection on tabular data W Brett Kennedy · Follow Published in Towards Data Science · 28 min read · 4 hours ago — This article is part of a series related to the challenges, and the techniques that may be used, to best identify outliers in data, including articles related to using PCA, Distance Metric Learning, Shared Nearest Neighbors, Frequent Patterns

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AI

The Difference Between ML Engineers and Data Scientists

Helping you decide whether you want to be a data scientist or machine learning engineer Egor Howell · Follow Published in Towards Data Science · 6 min read · 10 hours ago — Photo by Mohammad Rahmani on Unsplash A new role that has popped up in the tech space over the past few years is the machine learning engineer (MLE). Some people often confuse MLE with a data scientist; however, there is quite a

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Getting started with AI agents (part 2): Autonomy, safeguards and pitfalls

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In our first installment, we outlined key strategies for leveraging AI agents to improve enterprise efficiency. I explained how, unlike standalone AI models, agents iteratively refine tasks using context and tools to enhance outcomes such as code generation. I also discussed how multi-agent systems foster communication across departments, creating a unified user experience and driving productivity,

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JobQuest Assistant: How I Built a Smart Companion for Stress-Free Job Hunting With Coze | HackerNoon

Job hunting is so unbelievably draining. Yes! That’s not a cliché — It’s something I experienced. Moreover, a survey by ResumeGenius revealed that around 70% of job seekers feel more stressed about job hunting. 70% of job seekers feel more stressed about job hunting. Although there have been a lot of articles on how to make job hunting less stressful (Organize yourself, see it as a purpose, take breaks, create a portfolio, be selective, tailor

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Software

Here’s How to Make Decisions Based on Reality and How to Avoid Jumping to Conclusions | HackerNoon

We make hundreds of decisions throughout the day, most being automatic without much effort on our part. Information overload, cognitive biases, time constraints, and social and emotional influences can lead to quick and sometimes irrational conclusions. Our beliefs and worldviews act as filters that affect which information we pay attention to and how we interpret it. A lack of awareness can further make it challenging to recognize and control these automatic judgments. Critical thinking is

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Software

13 Years Later – 13 Reasons Why Remote Work Wasn’t Worth It | HackerNoon

My story is honest, but not perfect. What you like, take. What you don’t, neglect. No. 1 – Spend more time working from home than office. All the things I could’ve done, ppl seen, places been. No. 2 – Remote hype, down the wrong pipe. Ignored rumors about lack of glamours. No. 3 – Drew a line. Did the math. Volatility vs. stability. More money. Even more time without work. Net negative. No. 4 –

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HyperHuman Tops Image Generation Models in User Study | HackerNoon

Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: [email protected]; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem

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How Pose, Depth, and Surface-Normal Impact HyperHuman’s Image Quality | HackerNoon

Authors: (1) Xian Liu, Snap Inc., CUHK with Work done during an internship at Snap Inc.; (2) Jian Ren, Snap Inc. with Corresponding author: [email protected]; (3) Aliaksandr Siarohin, Snap Inc.; (4) Ivan Skorokhodov, Snap Inc.; (5) Yanyu Li, Snap Inc.; (6) Dahua Lin, CUHK; (7) Xihui Liu, HKU; (8) Ziwei Liu, NTU; (9) Sergey Tulyakov, Snap Inc. Table of Links Abstract and 1 Introduction 2 Related Work 3 Our Approach and 3.1 Preliminaries and Problem

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Microsoft’s 10 new AI agents strengthen its enterprise automation lead

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Microsoft made waves at Ignite 2024 with its announcement that 10 autonomous AI agents are now available for enterprise use. Microsoft effectively declared that AI agents are ready for prime time — achieving what others have yet to accomplish. Microsoft’s pre-built agents target core enterprise operations – from CRM and supply chain management to financial reconciliation.

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AI

Confidence Interval vs. Prediction Interval

A small but important difference that you should know Jonte Dancker · Follow Published in Towards Data Science · 8 min read · 12 hours ago — (Image by the author) In many data science-related tasks, we want to know how certain we are about the result. Knowing how much we can trust a result helps us to make better decisions. Once we have quantified the level of uncertainty that comes with a result we

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