Deep-learning Models

Deep-learning models are a type of artificial intelligence that utilizes complex algorithms to enable computers to learn and perform tasks without being explicitly programmed to do so. Deep-learning models are widely used in many applications, such as natural language processing, image recognition, autonomous vehicles, and recommender systems. By using multiple layers of processing, deep-learning models can accurately capture patterns in data and make decisions based on those patterns. As such, they are increasingly being used to analyze and interpret large amounts of data with high accuracy.

← Journal of Applied Robotics and Artificial Intelligence

Related Articles

9 article(s) found

A Role for in Vitro Disease Models in the Landscape of Preclinical Cardiotoxicity and Safety Testing

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Chest Wall Prostheses for Pectus Excavatum and Poland Syndrome Using 3D-Printed Models: Technique and Outcomes After 25 Years' Experience

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RETRACTED: Monte Carlo Approach To Genotype By Environment Interaction Models

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Biomedical Infophysical Models of Filtering Ghost Airflows by Wearing Masks and Maintaining Social Distancing to Prevent COVID-19 and Reopen All Systems after Shutdowns (Lockdowns)

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Models and data Analysis of the Outbreak Risk of COVID-19

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Histo-Morphological Effect of The Small, Large Intestines and Stomach of Animal Models Treated With Aqueous Extract of Abelmoschus Esculentus

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Time Series Analysis and Prediction of COVID-19 pandemic using Dynamic Harmonic Regression Models

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Parents and Siblings as Role Models in Dealing With Digital Screen Media. Findings from A Media Fasting Intervention

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Retraction Note: Monte Carlo Approach To Genotype By Environment Interaction Models

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