Deep learning

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Artificial Intelligence is transforming business and society, driven by three key trends: the rise of Big Data, advancements in affordable and powerful computing, and improved algorithms for data processing and learning. Deep learning, a subfield of AI, focuses on building large neural network models that make accurate, data-driven decisions. These modern neural networks excel at analyzing vast and complex datasets, making deep learning particularly well-suited for leveraging the rapid expansion of Big Data and computational capabilities. Over the past decade, deep learning has become essential in areas such as computer vision, speech recognition, information retrieval, machine translation, game AI, and autonomous vehicles. Its influence is expected to grow significantly in healthcare, finance, and smart cities in the coming years. This book provides a clear and comprehensive introduction to deep learning, detailing its evolution, capabilities, and future prospects. It covers key neural network architectures, including autoencoders, recurrent neural networks, long short-term memory networks, convolutional networks, and recent innovations like Generative Adversarial Networks, transformer networks, and capsule networks. Additionally, it explains the critical algorithms for training neural networks, specifically gradient descent and Backpropagation.