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).
What am I going to get from this course?
- 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
- Improved general understanding of Graph Models as Deep Learning concepts
- Basic working knowledge of the Python-based manipulation of Keras, Microsoft Cognitive Toolkit, Theano and TensorFlow deep learning platforms
- Basic ability to compare/contrast similar implementations of practical, graph-based solutions in Keras using Microsoft Cognitive Toolkit, Theano and/or TensorFlow back-end systems