| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Deep Learnıng and Applıcatıons | SWE 405 | 7 | 3 + 0 | 3 | 5 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | İngilizce |
| Dersin Seviyesi | Lisans |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Prof.Dr. DEVRİM AKGÜN |
| Dersi Verenler | |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Öğretim |
| Dersin Amacı | To understand the basics of deep learning, to use open source libraries about deep learning, to develop deep learning applications. |
| Dersin İçeriği | Mathematical background, tensor operations, Graident descent, backpropagation, Keras deeplearning library , Machine learning models, Convolutional neural networks (convnets), transfer learning ,metin verileriyle derin öğrenme, recurrent neural networks, 1D convnets , Keras functional API, Generative deep learning, current topics |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | To understand deep learning basics | Problem Solving, Project Based Learning, Lecture, Question-Answer, | Homework, Project / Design, |
| 2 | To understand neural network types | Project Based Learning, Problem Solving, | Project / Design, Homework, |
| 3 | To design, train and test deep learning models | Lecture, Problem Solving, | Project / Design, Homework, |
| 4 | To understand TensorFlow library basics | Lecture, Project Based Learning, | Project / Design, Homework, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Introduction, Artificial Intelligence, Machine Learning and Deep Learning | |
| 2 | Mathematical background, tensor operations, activation functions | |
| 3 | Gradient descent and variants, loss functions | |
| 4 | Feedforward networks and training, Keras deep learning library | |
| 5 | Data preprocessing, regularization methods | |
| 6 | Convolutional neural networks (convnets) | |
| 7 | Transfer learning | |
| 8 | Text processing, embedding layers | |
| 9 | Sequence processing, Recurrent neural networks (RNN) | |
| 10 | Simple RNN,LSTM, GRU | |
| 11 | Keras functional API | |
| 12 | Generative deep learning | |
| 13 | Contemporary deep learning topics | |
| 14 | Presentations |
| Kaynaklar | |
|---|---|
| Ders Notu | |
| Ders Kaynakları | Chollet, Francois. Deep learning with Python. Simon and Schuster, 2021. Ders Kaynakları Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016. |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions. | X | |||||
| 2 | An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations. | X | |||||
| 3 | An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently. | X | |||||
| 4 | An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods. | X | |||||
| 5 | An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research. | X | |||||
| 6 | Understanding, awareness of administration, control, development and security/reliability issues about information technologies. | ||||||
| 7 | An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility. | ||||||
| 8 | An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language. | ||||||
| 9 | An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology. | ||||||
| 10 | Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society. | ||||||
| 11 | An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications. | ||||||
| 12 | An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems. | ||||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 40 |
| 1. Ödev | 20 |
| 2. Ödev | 20 |
| 1. Proje / Tasarım | 20 |
| Toplam | 100 |
| 1. Final | 50 |
| 1. Yıl İçinin Başarıya | 50 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Assignment | 2 | 9 | 18 |
| Mid-terms | 1 | 13 | 13 |
| Project / Design | 1 | 14 | 14 |
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 2 | 32 |
| Toplam İş Yükü | 125 | ||
| Toplam İş Yükü / 25 (Saat) | 5 | ||
| Dersin AKTS Kredisi | 5 | ||