Course Details Language of Instruction English Level of Course Unit Bachelor's Degree Department / Program COMPUTER ENGINEERING Type of Program Formal Education Type of Course Unit Elective Course Delivery Method Face To Face Objectives of the Course Gain an understanding of deep learning architectures Learn the techniques used for developing deep learning models Develop skills for practical aspects of deep learning Course Content This course provides an introduction to deep learning. It covers deep architectures for multi-layer perceptrons, convolutional neural networks, and recurrent neural networks and practical applications of deep learning. Methods will be implemented by a software and applied on various machine learning problems. Course Methods and Techniques Grading Policy The final grades will be computed based on the general performance of the class and the distribution of grades (i.e. who deserves A and who deserves F). The grading strategy will be a combination of the standard catalogue grading and curve grading. Attendance Policy Each student is expected to attend to at least 50% of the theoretical classes. If not he/she will get NA as the final grade. Late Submission Policy It is the student s responsibility to follow the classes and do the assignments on time. Late submissions will be subject to a penalty of 25% if submitted within one week after the due date and %50 if submitted after one week. Make-Up Policy There are no make-ups in homework assignments, labs and quizzes. The student may be exempt from these assignments if a written and formal documentation is provided. Possible reasons for excused absences include serious illnesses, illness or death of a family member, university related trips and other serious circumstances. Acceptable documents for claiming an excused absence include medical doctor’s statements, petitions related to official university travels, court related documents, etc. If the student misses an exam (midterms or final) he or she can take a make-up exam upon submitting a formal document. Prerequisites and co-requisities None Course Coordinator Prof.Dr. VEHBİ ÇAĞRI GÜNGÖR cagri.gungor@agu.edu.tr Name of Lecturers None Assistants None Work Placement(s) No Recommended or Required Reading Resources Deep feedforward networks (LO1) Regularization for deep learning (LO1, LO2, LO3, LO4) Optimization for training deep models (LO1, LO2, LO3, LO4) Convolutional networks (LO1, LO2, LO3, LO4) Recurrent and recursive networks (LO1, LO2, LO3, LO4) Practical methodology of deep learning (LO1, LO2, LO3, LO4) Deep learning applications (LO1, LO2, LO3, LO4)

 





https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=74882&lang=en