Abstract: Deep learning, a subset of artificial intelligence, has revolutionized numerous fields by enabling machines to learn from large amounts of data. At its core, deep learning mimics the way the human brain processes information through artificial neural networks. These networks, composed of interconnected layers of nodes, extract intricate patterns and features from data, allowing machines to make predictions, classify information, and even generate content autonomously. From image and speech recognition to natural language processing and autonomous driving, deep learning has brought about groundbreaking advancements, pushing the boundaries of what machines can achieve.
Moreover, the versatility of deep learning extends beyond traditional domains, permeating into interdisciplinary fields such as healthcare, finance, and environmental science.
In healthcare, deep learning models analyze medical images to detect anomalies, aid in diagnosis, and even predict patient outcomes. Financial institutions utilize deep learning algorithms to analyze market trends, assess risks, and optimize investment strategies. Additionally, environmental scientists leverage deep learning techniques to analyze satellite imagery, monitor deforestation, and track climate change indicators, facilitating informed decision-making for sustainable development. As deep learning continues to evolve, its applications will likely expand, shaping the future of technology and driving innovation across various sectors.
Keywords: Neural Networks,Convolution Neural Networks (CNNs),Recurrent Neural Networks (RNNs),Generative Adversarial Networks (GANs)
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DOI:
10.17148/IJARCCE.2024.13640
