MATHEMATICAL ENGINEERING OF DEEP LEARNING

Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics The book provides a self contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning These ideas and architectures include deep neural networks convolutional models recurrent models long short term memory the attention mechanism transformers variational auto encoders diffusion models generative adversarial networks reinforcement learning and graph neural networks Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade The content is the foundation for state of the art artificial intelligence applications involving images sound large language models and other domains The focus is on the basic mathematical description of algorithms and methods and does not require computer programming The presentation is also agnostic to neuroscientific relationships historical perspectives and theoretical research The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning Key features A perfect summary of deep learning not tied to