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Get your copy on Amazon. A free online HTML version is available below.

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Read the book online:

• Front matter
• Chapter 1: Introduction
• Chapter 2: Principles of Machine Learning
• Chapter 3: Simple Neural Networks
• Chapter 4: Optimization Algorithms
• Chapter 5: Feedforward Deep Networks
• Chapter 6: Convolutional Neural Networks
• Chapter 7: Sequence Models
• Chapter 8: Specialized Architectures and Paradigms
• Epilogue
• Appendix A: Some Multivariable Calculus
• Appendix B: Cross Entropy and Other Expectations with Logarithms
• Back matter

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Synopsis: This book provides a complete and concise overview of the mathematical engineering of deep learning. In addition to overviewing deep learning foundations, the treatment includes convolutional neural networks, recurrent neural networks, transformers, generative adversarial networks, diffusion models, reinforcement learning, graphical neural networks, and multiple tricks of the trade. The focus is on the basic mathematical description of deep learning models, algorithms and methods. The presentation is mostly agnostic to computer code, neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such an approach is that a mathematically equipped reader can quickly grasp the essence of modern deep learning algorithms, models, and techniques without having to look at computer code, neuroscience, or the historical progression.

Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from the fields of engineering, signal processing, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.

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Mathematical background:

Navigating Mathematical Basics: A Primer for Deep Learning in Science

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Source code:

GitHub repo with source used to create figures and tables of the book. This includes code in Julia, Python, R, and TikZ.

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Cite:

@book{LiquetMokaNazarathy2024DeepLearning,
  title = {Mathematical Engineering of Deep Learning},
  author = {Benoit Liquet and Sarat Moka and Yoni Nazarathy},
  publisher = {CRC Press},
  year = {2024}
}

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Related courses and workshops:

• AMSI summer school 2021 course
• A crash course on using machine learning methods effectively in practice
• A workshop: Mathematical Engineering of Deep Learning - Part One Foundations

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