Graph Models for Deep Learning

This course provides a detailed executive-level review of contemporary topics in graph modeling theory with specific focus on Deep Learning theoretical concepts and practical applications. The ideal student is a technology professional with a basic working knowledge of statistical methods. Upon completion of this review, the student should acquire improved ability to discriminate, differentiate and conceptualize appropriate implementations of application-specific ('traditional' or 'rule-based') methods versus deep learning methods of statistical analyses and data modeling. Additionally, the student should acquire improved general understanding of graph models as deep learning concepts with specific focus on state-of-the-art awareness of deep learning applications within the fields of character recognition, natural language processing and computer vision. Optionally, the provided code base will inform the interested student regarding basic implementation of these models in Keras using Python (targeting TensorFlow, Theano or Microsoft Cognitive Toolkit). As an 'executive review', this text presents a distillation of essential information without the clutter of formulae, charts, graphs, references and footnotes. Thus, the student will not have a 'textbook' experience (or expense) while reviewing its contents. Instead, the student will quickly pass through a surprising wealth of actionable, easily-digestible technological information without the distraction of extemporaneous considerations.