Marktechpost

SEPTEMBER 24, 2024

Previously, researchers doubted that neural networks could solve complex visual tasks without hand-designed systems. However, this work demonstrated that with sufficient data and computational resources, deep learning models can learn complex features through a general-purpose algorithm like backpropagation.

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NYU Center for Data Science

SEPTEMBER 11, 2024

What sets Dr. Ho apart is her pioneering work in applying deep learning techniques to astrophysics. Ho’s innovative approach has led to several groundbreaking achievements: Her team at Carnegie Mellon University was the first to apply 3D convolutional neural networks in astrophysics.

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Viso.ai

FEBRUARY 11, 2024

Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. It is one of the largest resources available for training deep learning models in object recognition tasks. 2011 – A good ILSVRC image classification error rate is 25%.

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Viso.ai

MARCH 12, 2024

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). We split them into two categories – classical CV approaches, and papers based on deep-learning. Find the SURF paper here.

Computer Vision 52

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Viso.ai

JANUARY 16, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. The embedding functions can be convolutional neural networks (CNNs). Get a demo here. The CLIP model for ZSL shows 64.3%

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Viso.ai

MAY 31, 2024

Our software helps several leading organizations start with computer vision and implement deep learning models efficiently with minimal overhead for various downstream tasks. VOC2011 PASCAL VOC challenge took a big step forward in 2011 with VOC2011. provides a robust end-to-end computer vision infrastructure – Viso Suite.

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Lexalytics

APRIL 5, 2021

They were not wrong: the results they found about the limitations of perceptrons still apply even to the more sophisticated deep-learning networks of today. This book effectively killed off interest in neural networks at that time, and Rosenblatt, who died shortly thereafter in a boating accident, was unable to defend his ideas.

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