Dec 15, 2021 · Deep learning (DL), a prevailing branch of artificial intelligence (AI), has been extended with diversified network structures. The features of big data could be captured by DL automatically and efficiently.

Mar 23, 2024 · In computer vision, a series of exemplary advances have been made in several areas involving image classification, semantic segmentation, object detection, and image super-resolution reconstruction with the rapid development of deep convolutional neural network (CNN).

Aug 1, 2024 · In this article, we will delve into the fundamental concepts of deep learning for computer vision, exploring the architecture of convolutional neural networks, key techniques such as transfer learning, and notable applications that demonstrate the transformative potential of …

During the 10-week course, students will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the final assignment will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice.

Mar 25, 2020 · The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection ...

Nov 29, 2023 · Deep learning, particularly Convolutional Neural Networks (CNNs), overcomes these by learning directly from data, allowing for more accurate and versatile image recognition and classification.

Abstract: Significant strides have been achieved in the use of deep learning to computer vision, which has changed the way that computers process and respond to visual data.

Sep 17, 2024 · Deep learning, a subset of machine learning, has proven to be exceptionally effective in tackling complex computer vision tasks. By mimicking the human brain’s structure, these models can...

Apr 8, 2025 · Deep neural networks (DNNs) once showed increasing alignment with primate neural responses as they improved on computer vision benchmarks. This trend raised the exciting possibility that better models of biological vision would come as a byproduct of the deep learning revolution in artificial intelligence. However, the trend has reversed over recent years as DNNs have scaled to human or ...

This chapter discusses the basics of computer vision and determines how convolutional neural networks work. In recent years, deep learning achieved great momentum thanks to the ability to code certain properties into the architecture using Convolutional Neural Networks (CNNs), which are also called ConvNets.