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Deep Learning for Image Analyst – What’s New in ArcGIS Pro 3.2

ArcGIS

This blog details the new features and enhancements that were add for deep learning using the Image Analyst extension - for Pro 3.2.

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Fraud Detection using Deep Learning

Cloudera

The approach to machine learning using deep learning has brought marked improvements in the performance of many machine learning domains and it can apply just as well to fraud detection. The research team at Cloudera Fast Forward have written a report on using deep learning for anomaly detection.

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Best Deep Learning Tools to Master in 2023

Knowledge Hut

We all have witnessed how Deep learning has emerged as one of the most promising domains of artificial intelligence, enabling machines to process, analyze and draw insights from vast amounts of data. And hence, it has become significant to master some of the major deep learning tools to work with this concept better.

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Deep Learning with Nvidia GPUs in Cloudera Machine Learning

Cloudera

In our previous blog post in this series , we explored the benefits of using GPUs for data science workflows, and demonstrated how to set up sessions in Cloudera Machine Learning (CML) to access NVIDIA GPUs for accelerating Machine Learning Projects. Introduction.

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Deep Learning for Image Classification with Less Data

KDnuggets

In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.

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10 Common Applications of Deep Learning in Artificial Intelligence

ProjectPro

Perhaps the unwavering emergence of Deep Learning Applications on each passing day is the prove, maybe, we're already lodging in – into an advanced world. According to Markets and Markets, the deep learning application market was worth USD 2.28 billion in 2017 and is anticipated to be worth USD 18.16 And many more.

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Easily Deploy Deep Learning Models in Production

KDnuggets

This blog explores how to navigate these challenges. Getting trained neural networks to be deployed in applications and services can pose challenges for infrastructure managers. Challenges like multiple frameworks, underutilized infrastructure and lack of standard implementations can even cause AI projects to fail.