[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125090-en":3,"doc-seo-125090-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125090,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Deep Learning Techniques for Image Recognition - Machine Learning","Deep learning (DL), a major branch of machine learning (ML) and artificial intelligence (AI), has become a key driver for modern image recognition. Using multi-layer architectures such as convolutional neural networks (CNNs), DL enables systems to accurately identify and classify visual data, with benefits extending to speech recognition, translation, automated gameplay, healthcare diagnostics, and self-driving vehicles. Its effectiveness stems from learning hierarchical feature representations that support stronger extraction and pattern recognition. The paper also examines constraints including heavy reliance on large labelled datasets, limited common-sense reasoning, weak long-term planning, and decision-making challenges, and it reviews current methods, applications, strengths, and limitations.","Deep Learning Techniques for Image Recognition (Machine Learning) Kolapo Obanewa 1, Olumide Innocent Olope  \n1 Dundalk Institute of Technology  \nDublin Road, Dundalk, Co. Louth, A91 K584, Ireland  \nDOI: 10.22178/pos.110-8  \nLCC Subject Category: T1-995  \nReceived 26.09.2024 Accepted 28.10.2024 Published online 31.10.2024  \nCorresponding Author: Kolapo Obanewa  \n[kolapoobanewa@gmail.com](kolapoobanewa@gmail.com)  \n© 2024 The Authors. This article is licensed under a Creative Commons Attribution 4.0 License   \nAbstract. Deep learning (DL), a sophisticated subset of machine learning (ML), has emerged as a transformative force within the broader realm of artificial intelligence (AI). By leveraging architectures such as convolutional neural networks (CNNs), DL has significantly advanced image recognition capabilities, enabling systems to identify and classify visual data with remarkable precision accurately. This technology is not only applicable to image recognition. Still, it has also made strides in diverse areas, such as speech recognition, language translation, automated gameplay, healthcare diagnostics, and the development of self-driving vehicles. The success of DL in this domain can be attributed to its ability to learn hierarchical representations of data, allowing for improved feature extraction and pattern recognition. Despite its impressive performance, deep learning is not without its limitations. Key challenges include its reliance on vast amounts of labelled data, which can be difficult and expensive to obtain, its lack of common sense reasoning and difficulties in addressing complex, multifaceted problems.  \nAdditionally, DL models often struggle with long-term planning and decision-making, which can hinder their effectiveness in certain applications. This paper delves into the significant role of deep learning in image recognition, providing a comprehensive overview of its methodologies, applications, strengths, and limitations. By examining current advancements and ongoing challenges, this work aims to contribute to understanding deep learning's impact on the field and its future potential.  \nKeywords: Deep Learning; Machine Learning; Image Recognition; Data Requirements; Interpretability; Computational Resources; Overfitting; Adversarial Attacks.  \nINTRODUCTION  \nDeep learning (DL) has emerged as a transformative approach within the broader context of machine learning (ML) and artificial intelligence (AI). By utilising multi-layered neural networks, particularly convolutional neural networks (CNNs), DL has shown exceptional capabilities in processing and analysing vast amounts of data. Its impact is particularly profound in image recognition, which has enabled significant advancements in accuracy and efficiency [1]. Deep learning can be traced back to pioneers such as Alexey Ivakhnenko, who introduced the idea of multi-layered neural networks in the 1960s [2]. However, it was not until the advent of powerful computational resources and large datasets that  \ndeep learning began to gain traction in the research community, particularly after 2010. Breakthroughs in techniques such as dropout have fueled this resurgence, rectified linear units (ReLU), and transfer learning, which have collectively improved model performance [3]. In addition to image recognition, deep learning has found applications in various domains, including natural language processing, autonomous driving, and healthcare [4]. Despite its remarkable successes, deep learning is not without limitations. Challenges such as data dependence, interpretability issues, and a lack of common sense reasoning remain significant hurdles researchers continue to address [5]. This paper aims to provide a comprehensive overview of deep learn-  \ning's role in image recognition, exploring its methodologies, successes, and inherent challenges. By critically examining the current landscape of deep learning, we seek to illuminate its potential and limitations in advancin","cbCaijWhnRlrW0Fx","https://ap.wps.com/l/cbCaijWhnRlrW0Fx","pdf",457563,1,8,"English","en",105,"# Introduction\n# Literature review","[{\"question\":\"What core role does deep learning play in image recognition?\",\"answer\":\"Deep learning improves image recognition by using multi-layer neural architectures, especially CNNs, to learn hierarchical representations that enhance feature extraction and pattern recognition.\"},{\"question\":\"Which developments helped deep learning gain momentum after 2010?\",\"answer\":\"Increases in computational resources and large datasets drove adoption, while techniques such as dropout, ReLU, and transfer learning improved model performance and training stability.\"},{\"question\":\"What major limitations does the paper highlight for deep learning?\",\"answer\":\"Key issues include dependence on vast labelled data, interpretability difficulties, lack of common-sense reasoning, and challenges in long-term planning and decision-making.\"}]","Deep Learning Techniques for Image Recognition - 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