Advancements and Applications of Deep Learning: A Comprehensive Review
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Abstract
Artificial intelligence's significant branch of deep learning has grown tremendously in the past few years. It has integrated into various fields due to its enhanced predictive and analytical features. Due to this technology's capability of making sense of massive amounts of data, it has found application across a broad spectrum of industries, including the healthcare and financial sectors, to name but a few. This review aims to identify and present the significant developments in deep learning methods and the wide range of fields to which they are applied. The key concern is what has happened to these processes, how these changes have affected professional practices, and the nature of future technological environments. It systematically identifies the literature based on scientific databases like IEEE Xplore, PubMed, and Google Scholar. The search used the following keywords: 'deep learning advancements,' 'neural networks in practice,' and 'AI applications in industry. 'Applying strict inclusion and exclusion criteria helped to choose more relevant and reliable works. The presented results focus on the main achievements in deep learning structures, especially convolutional and recurrent neural networks, and their application in real-life use cases. Some areas where deep learning is applied include diagnosing medical conditions, self-driving cars, and even predicting the trends in financial markets. Deep learning has dramatically impacted technological advancement and set new standards by bringing significant economic and social value improvements. The field has not been out of finding data privacy and the lack of model interpretability, which must be solved to ensure future growth.
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