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Deep Learning Program Description In this course we will learn about the basics of deep neural networks, and their applications to various tasks. The course aim is to present the mathematical, statistical, and computational challenges of building stable representations for high -dimensional data. Course Objectives • Learn the tools required for building Deep Learning models. • Explore multiple architectures and understand how to fine-tune and continuously improve models • Learn how the same task can be solved using multiple Deep Learning approaches Course Delivery Option: Classroom, Online/Distance Learning/Distance Learning Sequence and frequency of class sessions: Every 6 weeks Related Job Titles/Occupations Software Developers, Applications SCO Code 15-1132.00 Instruction Details Students will have significant familiarity with the subject and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study. Program In Duration: Course Name Clock Hours Duration Maximum Completion Time Deep Learning 240 hours 12 weeks 15 weeks Pre -Requisite: Basic Computer Inowledge Instructional Material: Textbook Deep Learning (Adaptive Computation and Machine Learning series) Part of Adaptive Computation and Machine Learning series (21 Books) I by Ian Goodfellow , Yoshua Bengio , et al. Total Charges for Period of Attendance & Estimated char es for entire urogram• $4 877 50 Tuition Fee: $4, 725; Registration Fee: $77.50 (Non-refundable); Book: $75.00 Requirement for completing the program End of the program students are required to complete all the projects and lab work assigned during the program. Student will get the course completion certificate after completing the examination and the projects. Student may take the Deep Learning certification exam. 01