Tue.Jul 30, 2019

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4 Data Goldmines Your Company Should Not Ignore

Smart Data Collective

In an earlier age, perhaps as little as a decade ago, businesses had to rely on intuition and educated guesses to guide their spending. The situation was famously captured by John Wanamaker, who said, “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.” Today, data is everywhere. Phones track our locations and our social media usage.

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Understanding Tensor Processing Units

KDnuggets

The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.

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Today’s Biggest Cyber Security Threat is Inside Your Business

Smart Data Collective

Computer breaches from Russian or Chinese hackers get the headlines, but the reality is you are more likely to be a victim from an insider. It turns out that as much as 60 percent of all attacks were carried out by insiders – either overtly or inadvertently. The High Cost of Breaches. If it’s your business that falls victim, the cost can be high. Your company’s reputation can be damaged.

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Here’s how you can accelerate your Data Science on GPU

KDnuggets

Data Scientists need computing power. Whether you’re processing a big dataset with Pandas or running some computation on a massive matrix with Numpy, you’ll need a powerful machine to get the job done in a reasonable amount of time.

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From Developer Experience to Product Experience: How a Shared Focus Fuels Product Success

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

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Big Data Joins The Fight Against Traumatic Brain Injuries

Smart Data Collective

Big data is being used more frequently in healthcare facilities all over the world. One report shows that the global market for big data in healthcare is expected to reach $68.75 billion in 2025. However, this figure misses some important nuances, such as which areas of medicine are using big data the most. Some medical procedures have relied on it more than others, while others are just exploring its potential.

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P-values Explained By Data Scientist

KDnuggets

This article is designed to give you a full picture from constructing a hypothesis testing to understanding p-value and using that to guide our decision making process.

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A Data Science Playbook for explainable ML/xAI

KDnuggets

This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.

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How To Reinvent Your FP&A Process

Jedox

There has been a lot of talk about changing FP&A processes yet little real action in this area. Budgeting? How many do zero-based budgeting or pure beyond budgeting? Most are stuck doing the traditional one-year budget and five-year plan. Analysis? Wishful thinking. Are you doing what-if scenarios or Monte Carlo simulations or simply just plotting your data in a time series into a graph and showing it to your stakeholders?

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Why Multi-Dimensional Personalization is Worth the Investment

Teradata

It's not enough just to drive personalization in your marketing efforts, you need to take a multi-dimensional approach. Find out why it's worth the investment.

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Does Your Business Need Data Analytics Outsourcing?

ScienceSoft

We explore data analytics outsourcing – outline the service, look at the advantages that it can bring to businesses, as well as explore the aspects that usually raise concerns.

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Peak Performance: Continuous Testing & Evaluation of LLM-Based Applications

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.