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Comprehensive Guide: Top Computer Vision Resources All in One Blog

Mlearning.ai

Save this blog for comprehensive resources for computer vision Source: appen Working in computer vision and deep learning is fantastic because, after every few months, someone comes up with something crazy that completely changes your perspective on what is feasible.

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A Vision for the Future: How Computer Vision is Transforming Robotics

Heartbeat

The goal of computer vision research is to teach computers to recognize objects and scenes in their surroundings. In this article, I would like to take a look at the current challenges in the field of robotics and discuss the relevance and applications of computer vision in this area.

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AI trends in 2023: Graph Neural Networks

AssemblyAI

Top 50 keywords in submitted research papers at ICLR 2022 ( source ) A recent bibliometric study systematically analysed this research trend, revealing an exponential growth of published research involving GNNs, with a striking +447% average annual increase in the period 2017-2019.

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Computer Vision and Deep Learning for Healthcare

PyImageSearch

This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. Computer Vision and Deep Learning for Healthcare Benefits Unlocking Data for Health Research The volume of healthcare-related data is increasing at an exponential rate.

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Computer Vision in Autonomous Vehicle Systems

Viso.ai

Computer vision is a key component of self-driving cars. In this article, we’ll elaborate on how computer vision enhances these cars. To accomplish this, they require two key components: machine learning and computer vision. The eyes of the automobile are computer vision models.

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Making Sense of the Mess: LLMs Role in Unstructured Data Extraction

Unite.AI

Recent advancements in hardware such as Nvidia H100 GPU, have significantly enhanced computational capabilities. With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. Subsequently, some RNNs were also trained using GPUs, though they did not yield good results.

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What’s New in PyTorch 2.0? torch.compile

Flipboard

torch.compile Over the last few years, PyTorch has evolved as a popular and widely used framework for training deep neural networks (DNNs). Since the launch of PyTorch in 2017, it has strived for high performance and eager execution. In this series, you will learn about Accelerating Deep Learning Models with PyTorch 2.0.