Electrical and Computer Engineering Graduate With Thesis
Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
Graduate Education Institute
Programme of Electrical and Computer Engineering Graduate With Thesis
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 80511125 | Deep Learning | 2023 | Spring | 2 | 3+0+0 | 7,5 | 7,5 |
| Course Type | Elective |
| Course Cycle | Master's (Second Cycle) (TQF-HE: Level 7 / QF-EHEA: Level 2 / EQF-LLL: Level 7) |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Asst. Prof. Ali Osman ÇIBIKDİKEN |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Hüseyin Oktay Altun | - |
Course Content
Introduction to Deep Learning, Logical Classification, Random Optimization, Softmax and TensorFlow, Data and Parameter Setting,Stability Analysis and Verification Set, Deep Neural Networks, Inside Neural Networks, Unsupervised Learning, Meaning Uncertainty, ""Placement"" s, Recurrent Neural Networks, Long - Short Memory, Regularization
Objectives of the Course
The course aims to provide the theoretical and practical background for the application of deep learning on artificial neural networks.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to Deep Learning, |
| 2 | Logical Classification, |
| 3 | Random Optimization, |
| 4 | Softmax and TensorFlow, |
| 5 | Data and Parameter Setting, |
| 6 | Stability Analysis and Verification Set, |
| 7 | Deep Neural Networks, |
| 8 | Inside Neural Networks, |
| 9 | Unsupervised Learning, |
| 10 | Meaning Uncertainty |
| 11 | ""Placement"" s, |
| 12 | Recurrent Neural Networks, |
| 13 | Long - Short Memory, |
| 14 | Regularization |
Textbook or Material
| Resources | TensorFlow Machine Learning Cookbook - Nick McClure |
| TensorFlow Machine Learning Cookbook - Nick McClure | |
| TensorFlow Machine Learning Cookbook - Nick McClure | |
| TensorFlow Machine Learning Cookbook - Nick McClure | |
| TensorFlow Machine Learning Cookbook - Nick McClure | |
| TensorFlow Machine Learning Cookbook - Nick McClure |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||