глибоке навчання

UNDERSTANDING LARGE LANGUAGE MODELS: THE FUTURE OF ARTIFICIAL INTELLIGENCE

The article examines the newest direction in artificial intelligence - Large Language Models, which open a new era in natural language processing, providing the opportunity to create more flexible and adaptive systems. With their help, a high level of understanding of the context is achieved, which enriches the user experience and expands the fields of application of artificial intelligence. Large language models have enormous potential to redefine human interaction with technology and change the way we think about machine learning.

Review of disease identification methods based on computed tomography imagery

Methods and approaches to computational diagnosis of various pulmonary diseases via automated analysis of chest images performed with computed tomography were reviewed. Google Scholar database was searched with several queries focused on deep learning and machine learning chest computed tomography imagery analysis studies published during or after 2017. A collection of 39 papers was collected after screening the search results. The collection was split by publication date into two separate sets based on the date being prior to or after the start of the COVID-19 pandemic.

RESEARCH OF PLANT DISEASE DIAGNOSTIC METHODS USING DEEP LEARNING

The article explores the use of convolutional neural networks (CNNs) in the diagnosis and identification of plant diseases and pests. Various methods of plant disease diagnosis, features of datasets, and challenges in this research direction are considered. The article discusses a five-step methodology for determining plant diseases, including data collection, preprocessing, segmentation, feature extraction, and classification. Different deep learning architectures enabling fast and efficient plant disease diagnosis are investigated.

Research of the models for sign gesture recognition using 3D convolutional neural networks and visual transformers

The work primarily focuses on addressing the contemporary challenge of hand gesture recognition, driven by the overarching objectives of revolutionizing military training methodologies, enhancing human-machine interactions, and facilitating improved communication between individuals with disabilities and machines. In-depth scrutiny of the methods for hand gesture recognition involves a comprehensive analysis, encompassing both established historical computer vision approaches and the latest deep learning trends available in the present day.