Analytics Vidhya

NOVEMBER 25, 2022

This article was published as a part of the Data Science Blogathon. Source:pixabay.com Introduction State-of-the-art machine learning models and artificially intelligent machines are made of complex processes like adjusting hyperparameters and choosing models that provide better accuracy and the metrics that govern this behavior. To achieve this result manually, many experiments using a lot of […].

Machine Learning 302

Machine Learning Metrics Data Science Modeling 302

KDnuggets

NOVEMBER 25, 2022

The final article in this nine-part series summarizes the many reasons why Machine Learning is not like your brain - along with a few similarities.

Machine Learning 153

Machine Learning 153

Analytics Vidhya

NOVEMBER 25, 2022

Introduction In a continually disruptive space of data science, life sciences, and analytics, staying abreast with the newest trends and happenings in the industry is vital. In an endeavour to keep the audiences engaged and updated, Fractal hosts a series of podcasts frequently with the mavericks of the data science industry. Sagar Shah, the Client Partner […].

Data Processing 266

Data Processing Data Science Analytics 266

KDnuggets

NOVEMBER 25, 2022

How to enable AI models to have similar capabilities: to communicate, to ground, and to learn from language.

Modeling 108

Modeling 108

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Experimentation

MLWhiz

NOVEMBER 25, 2022

Parallelism and concurrency aren’t the same things. In some cases, concurrency is much more powerful.

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Dataiku

NOVEMBER 25, 2022

There are some very good reasons to be interested in predictive maintenance. Think of the time saved! The costs cut! The resources better allocated! The more efficient you become at identifying which part, machine, or process is likely to break down and when, the more reliably you can reap the benefits.

Cost-Benefit 40

Cost-Benefit 40

Ontotext

NOVEMBER 25, 2022

SeeNews : Mr. Kiryakov, semantic technology has been increasingly on the rise in popularity in recent years. Have the sectors where it is most commonly used changed, or is the list still the same as it was four or five years ago? Atanas Kyriakov : A lot has changed. I will give you an example of enterprises from the life sciences and healthcare verticals.

Technology 52

Technology Management Manufacturing Publishing 52