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dubnov s lecture notes deep learning insights artificial mind 2026

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Textbook in PDF format The compendium provides an introduction to the theory of Deep Learning, from basic principles of neural network modeling and optimization to more advanced topics of neural networks as Gaussian processes, neural tangent and information theory. This unique reference text complements a largely missing theoretical introduction to neural networks without being overwhelmingly technical in a level accessible to upper-level undergraduate engineering students. Advanced chapters were designed to offer an additional intuition into the field by explaining Deep Learning from statistical and information theory perspectives. The book further provides additional intuition to the field by relating it to other statistical and information modeling approaches. This book aims to provide a comprehensive introduction to Deep Learning, a vibrant subfield of Machine Learning that has quickly become a cornerstone of modern Artificial Intelligence. The motivation for writing this text originated when we co-taught a Deep Learning course for senior students at Duke Kunshan University (DKU). We aimed to provide a broad introduction that covers the main mathematical concepts in Deep Learning in a level accessible to upper-level undergraduate engineering students. Advanced chapters were designed to offer an additional intuition into the field by linking Deep Learning to broader statistical and information modeling approaches. This book contains six parts; each is roughly designed to progressively build upon the previous ones. It starts with basic concepts and leads up to more specialized discussions. The first part of this book presents basics of neural networks. We begin with the simplest neural network architectures and quickly demonstrate practical implementations, which are useful for readers keen to apply these concepts to real-world data. This section also addresses why neural networks are particularly effective and discusses the optimization techniques uniquely suited to these models. The second part focuses on autoencoders and variational autoen-coders, which are fundamental tools for learning representations for data. This part connects these ideas back to classical techniques like PCA and probabilistic PCA, which provide a deeper understanding of data representation. The third part deals with special neural network architectures that have been critical to the current advancements in AI. These include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. We explain their development and applications in vision, audio, and natural language processing. The fourth part presents generative models. We expand upon variational autoencoders (VAEs) and then study generative adversarial networks (GANs). These are fundamental generative models which relate to very interesting mathematical and statistical theories. Further, we study normalizing flows and diffusion models, which are driving recent developments in AIGC (artificial intelligence generated content). The fifth part introduces more theoretical part of deep learning. Rather than focusing on applications of neural networks, this part is dedicated to understanding the theories behind neural networks, situating them within the broader context of traditional learning theory. The sixth part contains some further topics we believe to be of critical importance, including transfer learning, explainable AI, and deep reinforcement learning

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Dubnov S. Lecture Notes in Deep Learning...Insights Into an Artificial Mind 2026.pdf 27.3 MB

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dubnov s lecture notes deep learning insights artificial mind 2026

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