Viso.ai

JANUARY 2, 2024

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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AssemblyAI

SEPTEMBER 22, 2023

We’ll assume some general familiarity with machine learning concepts. Data scarcity: Paired natural anguage descriptions of music and corresponding music recordings are extremely scarce, in contrast to the abundance of image/descriptions pairs available online, e.g. in online art galleries or social media.

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Pickl AI

FEBRUARY 1, 2023

What if we say that you have the option of using a pre-trained model that works as a framework for data training? Yes, Transfer Learning is the answer to it. What is Transfer Learning? Transfer Learning is a technique in Machine Learning where a model is pre-trained on a large and general task.

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Heartbeat

NOVEMBER 27, 2023

Deep learning automates and improves medical picture analysis. 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.

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Viso.ai

DECEMBER 6, 2023

Our solution enables leading companies to use a variety of machine learning models and tasks for their computer vision systems. For instance, CV algorithms can understand Light Detection and Ranging (LIDAR) data for enhanced perceptions of the environment. YOLOv7 is a recent iteration of the YOLO network. Get a demo here.

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Viso.ai

JANUARY 16, 2024

The traditional machine learning (ML) paradigm involves training models on extensive labeled datasets. However, the method requires a sufficient volume of labeled training data. The embedding functions can be convolutional neural networks (CNNs).

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Pickl AI

SEPTEMBER 15, 2024

Overview of the Components The Siamese Neural Network architecture consists of multiple identical subnetworks that process input pairs to determine their similarity. This design enables efficient learning from minimal data, making it ideal for tasks like facial recognition and signature verification, where data scarcity is a challenge.

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