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Convolutional Neural Networks: A Deep Dive (2024)

Viso.ai

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

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Deep Learning for Medical Image Analysis: Current Trends and Future Directions

Heartbeat

Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs. Deep learning automates and improves medical picture analysis.

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What is Transfer Learning in Deep Learning? [Examples & Application]

Pickl AI

It helps in overcoming some of the drawbacks and bottlenecks of Machine Learning: Data scarcity: Transfer Learning technology doesn’t require reliance on larger data sets. This technology allows models to be fine-tuned using a limited amount of data.

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Computer Vision Tasks (Comprehensive 2024 Guide)

Viso.ai

Computer vision (CV) is a rapidly evolving area in artificial intelligence (AI), allowing machines to process complex real-world visual data in different domains like healthcare, transportation, agriculture, and manufacturing. The purpose is to give you an idea of modern computer vision algorithms and applications. Get a demo here.

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N-Shot Learning: Zero Shot vs. Single Shot vs. Two Shot vs. Few Shot

Viso.ai

The techniques make AI model development scalable and computationally inexpensive, as you can build large models with several parameters to capture general data patterns from a few samples. The former simply means synthesizing more data for training tasks using generative and augmentation methods. Let’s discuss each in more detail.