Remove 2011 Remove Computer Vision Remove Deep Learning
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Understanding the different types and kinds of Artificial Intelligence

IBM Journey to AI blog

In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training. For example, Apple made Siri a feature of its iOS in 2011. This early version of Siri was trained to understand a set of highly specific statements and requests.

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Amr Nour-Eldin, Vice President of Technology at LXT – Interview Series

Unite.AI

research scientist with over 16 years of professional experience in the fields of speech/audio processing and machine learning in the context of Automatic Speech Recognition (ASR), with a particular focus and hands-on experience in recent years on deep learning techniques for streaming end-to-end speech recognition.

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Top Computer Vision Papers of All Time (Updated 2024)

Viso.ai

Today’s boom in computer vision (CV) started at the beginning of the 21 st century with the breakthrough of deep learning models and convolutional neural networks (CNN). In this article, we dive into some of the most significant research papers that triggered the rapid development of computer vision.

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Computer Vision for Cultural Heritage Preservation: Unlocking the Past with Advanced Imaging…

Heartbeat

Computer Vision for Cultural Heritage Preservation: Unlocking the Past with Advanced Imaging Technology Image Source: Technology Innovators Preserving our cultural legacy is critical because it allows us to remain in touch with our past, learn our roots, and appreciate humanity's rich history.

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Low Code and No Code Platforms for AI and Computer Vision

Viso.ai

Low code and no code for AI Business benefits of platforms About us: At viso.ai, we power Viso Suite , the leading no-code/low-code computer vision platform. Our technology is used by leaders worldwide to rapidly develop, deploy and scale real-time computer vision systems. The idea of low-code was introduced in 2011.

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Revolutionizing Image Classification: Training Large Convolutional Neural Networks on the ImageNet Dataset

Marktechpost

The success of this model reflects a broader shift in computer vision towards machine learning approaches that leverage large datasets and computational power. This breakthrough marks a paradigm shift in object recognition, paving the way for more powerful and data-driven models in computer vision.

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The Evolution of ImageNet and Its Applications

Viso.ai

This database has undoubtedly played a great impact in advancing computer vision software research. It is a technique used in computer vision to identify and categorize the main content (objects) in a photo or video. The other usage of image datasets is as a benchmark in computer vision algorithms.