Course Name | Code | Semester | T+U Hours | Credit | ECTS |
---|---|---|---|---|---|
Introduction To Deep Learning | ENF 547 | 0 | 3 + 0 | 3 | 6 |
Precondition Courses | |
Recommended Optional Courses | |
Course Language | Turkish |
Course Level | yuksek_lisans |
Course Type | Optional |
Course Coordinator | Doç.Dr. DEVRİM AKGÜN |
Course Lecturers | |
Course Assistants | |
Course Category | Field Proper Education |
Course Objective | To teach mathematical fundamentals about deep learning, to use open source libraries related to deep learning, to develop deep learning applications. |
Course Content | 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 |
# | Course Learning Outcomes | Teaching Methods | Assessment Methods |
---|---|---|---|
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, |
Week | Course Topics | Preliminary Preparation |
---|---|---|
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 |
Resources | |
---|---|
Course Notes | <p>Weekly slides</p> |
Course Resources | Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016. Chollet, Francois. Deep learning with python. Manning Publications Co., 2017. |
Order | Program Outcomes | Level of Contribution | |||||
---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | |||
1 | X | ||||||
2 | X | ||||||
3 | X | ||||||
4 | X | ||||||
5 | X | ||||||
6 | X | ||||||
8 | X | ||||||
9 | X |
Evaluation System | |
---|---|
Semester Studies | Contribution Rate |
1. Ara Sınav | 60 |
1. Proje / Tasarım | 20 |
1. Ödev | 10 |
2. Ödev | 10 |
Total | 100 |
1. Yıl İçinin Başarıya | 50 |
1. Final | 50 |
Total | 100 |
ECTS - Workload Activity | Quantity | Time (Hours) | Total Workload (Hours) |
---|---|---|---|
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 |
Total Workload | 151 | ||
Total Workload / 25 (Hours) | 6.04 | ||
dersAKTSKredisi | 6 |