| Ders Adı | Kodu | Yarıyıl | T+U Saat | Kredi | AKTS |
|---|---|---|---|---|---|
| Introductıon To Deep Learnıng | ENF 547 | 0 | 3 + 0 | 3 | 6 |
| Ön Koşul Dersleri | |
| Önerilen Seçmeli Dersler | |
| Dersin Dili | Türkçe |
| Dersin Seviyesi | YUKSEK_LISANS |
| Dersin Türü | Seçmeli |
| Dersin Koordinatörü | Prof.Dr. DEVRİM AKGÜN |
| Dersi Verenler | |
| Dersin Yardımcıları | |
| Dersin Kategorisi | Alanına Uygun Öğretim |
| Dersin Amacı | To teach mathematical fundamentals about deep learning, to use open source libraries related to deep learning, to develop deep learning applications. |
| Dersin İçeriği | Mathematical foundations, tensor operations, Graident descent, backpropagation, Keras deeplearning library and examples of usage, Models of machine learning, Convolution neural networks (convnets), feature extraction with pre-trained convnet , convnet visualization, deep learning with text data , recurrent neural networks, 1D convnets for array processing, Keras functional API, Keras functions, TensorBoard visualization tool, Generative deep learning, Contemporary issues |
| # | Ders Öğrenme Çıktıları | Öğretim Yöntemleri | Ölçme Yöntemleri |
|---|---|---|---|
| 1 | Knows the basic topics about deep learning | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, |
| 2 | Knows 1D and 2D convnet-convolution layers. | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, |
| 3 | Uses open source libraries for deep learning | Lecture, Question-Answer, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, |
| 4 | Knows text processing, Embedding Layer, Simple RNN, LTSM and GRU layers | Lecture, Drilland Practice, Problem Solving, Project Based Learning, | Testing, Homework, Project / Design, |
| Hafta | Ders Konuları | Ön Hazırlık |
|---|---|---|
| 1 | Overview of artificial intelligence, machine learning and deep learning | Slides |
| 2 | Mathematical foundations, Gradient descent algorithms, loss functions, backpropagation. | Slides |
| 3 | Tensor operations with Python, Keras deep learning library, | Slides |
| 4 | Multi-label classification, Regression | Slides |
| 5 | Data preprocessing, overfitting prevention, weight regularization, dropout | Slides |
| 6 | 2-Dimensional Convolution (conv2D) Neural Networks (convnets), pooling | Slides |
| 7 | Augmentation of image data, pretrained networks | Slides |
| 8 | Fine tuning, convolution filters visualization | Slides |
| 9 | Deep learning with text data, Embedding layers | Slides |
| 10 | Recurrent neural networks | Slides |
| 11 | LSTM and GRU layers | Slides |
| 12 | Array processing with 1D convnets | Slides |
| 13 | Keras functional API, Multiple input or multiple output models | Slides |
| 14 | Generative deep learning | Slides |
| Kaynaklar | |
|---|---|
| Ders Notu | Weekly slides |
| Ders Kaynakları | Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016. Chollet, Francois. Deep learning with python. Manning Publications Co., 2017. |
| Sıra | Program Çıktıları | Katkı Düzeyi | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | X | ||||||
| 2 | X | ||||||
| 3 | X | ||||||
| 4 | X | ||||||
| 5 | X | ||||||
| 6 | X | ||||||
| 8 | X | ||||||
| 9 | X | ||||||
| Değerlendirme Sistemi | |
|---|---|
| Yarıyıl Çalışmaları | Katkı Oranı |
| 1. Ara Sınav | 60 |
| 1. Proje / Tasarım | 20 |
| 1. Ödev | 10 |
| 2. Ödev | 10 |
| Toplam | 100 |
| 1. Yıl İçinin Başarıya | 50 |
| 1. Final | 50 |
| Toplam | 100 |
| AKTS - İş Yükü Etkinlik | Sayı | Süre (Saat) | Toplam İş Yükü (Saat) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 16 | 3 | 48 |
| Hours for off-the-classroom study (Pre-study, practice) | 16 | 3 | 48 |
| Mid-terms | 1 | 15 | 15 |
| Assignment | 2 | 5 | 10 |
| Project / Design | 1 | 10 | 10 |
| Final examination | 1 | 20 | 20 |
| Toplam İş Yükü | 151 | ||
| Toplam İş Yükü / 25 (Saat) | 6,04 | ||
| Dersin AKTS Kredisi | 6 | ||