📮 Foundations of Deep Learning by Fengxiang He (.PDF)

Deep Learning has significantly reshaped a variety of technologies, such as image processing, natural language processing (NLP), and audio processing. The excellent generalizability of Deep Learning is like a “cloud” to conventional complexity-based learning theory: the over-parameterization of Deep Learning makes almost all existing tools vacuous. This irreconciliation considerably undermines the confidence of deploying Deep Learning to security-critical areas, including autonomous vehicles and medical diagnosis, where small algorithmic mistakes can lead to fatal disasters. This book seeks to explaining the excellent generalizability, including generalization analysis via the size-independent complexity measures, the role of optimization in understanding the generalizability, and the relationship between generalizability and ethical/security issues. We expect readers can have a big picture of the current knowledge in Deep Learning theory, understand how the Deep Learning theory can guide new algorithm designing, and identify future research directions. Readers need knowledge of calculus, linear algebra, probability, statistics, and statistical learning theory.

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