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Navigating tomorrow: Role of AI and ML in information technology

Dataconomy

With the ability to analyze a vast amount of data in real-time, identify patterns, and detect anomalies, AI/ML-powered tools are enhancing the operational efficiency of businesses in the IT sector. Why does AI/ML deserve to be the future of the modern world? Let’s understand the crucial role of AI/ML in the tech industry.

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Your guide to generative AI and ML at AWS re:Invent 2023

AWS Machine Learning Blog

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. In addition to several exciting announcements during keynotes, most of the sessions in our track will feature generative AI in one form or another, so we can truly call our track “Generative AI and ML.”

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Deep Learning for NLP: Word2Vec, Doc2Vec, and Top2Vec Demystified

Mlearning.ai

It was first introduced in 2013 by a team of researchers at Google led by Tomas Mikolov. Word2Vec is a shallow neural network that learns to predict the probability of a word given its context (CBOW) or the context given a word (skip-gram). I hope you find this article to be helpful. If you’d like, add me on LinkedIn !

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16 Companies Leading the Way in AI and Data Science

ODSC - Open Data Science

To deliver on their commitment to enhancing human ingenuity, SAS’s ML toolkit focuses on automation and more to provide smarter decision-making. Plotly In the time since it was founded in 2013, Plotly has released a variety of products including Plotly.py, which, along with Plotly.r,

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sktime?—?Python Toolbox for Machine Learning with Time Series

ODSC - Open Data Science

Here’s what you need to know: sktime is a Python package for time series tasks like forecasting, classification, and transformations with a familiar and user-friendly scikit-learn-like API. Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!)

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1x1 Convolution: Explainer

Mlearning.ai

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture.

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You are probably doing Medical Imaging AI the wrong way.

Mlearning.ai

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. ML practitioners, believing they had to match the sheer size of ImageNet, refrained from pre-training with much smaller available medical image datasets, let alone developing new ones.

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