Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
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Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning Summary
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May 03, 2025
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"Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning" sounds like a comprehensive guide aimed at providing readers with a foundational understanding of the mathematical principles underlying deep learning. Such a book would likely cover topics such as linear algebra, calculus, probability theory, and optimization methods, all of which are crucial for understanding the workings of neural networks and other AI algorithms. The book might start with an introduction to basic mathematical concepts and then gradually delve into more advanced topics specific to deep learning, such as backpropagation, gradient descent, activation functions, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), among others. Additionally, it may include practical examples, exercises, and code snippets to help readers apply the mathematical concepts to real-world AI problems. Overall, such a resource would serve as a valuable tool for students, researchers, and practitioners looking to gain a deeper understanding of the mathematical foundations of deep learning.
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