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, Problem Solving, Project Based Learning, Testing, Homework, Project / Design,
3 Uses open source libraries for deep learning Lecture, Question-Answer, Problem Solving, Project Based Learning, Testing, Homework, Project / Design,
4 Knows text processing, Embedding Layer, Simple RNN, LTSM and GRU layers Lecture, Question-Answer, 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
7 X
8 X
9 X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 60
1. Ödev 10
2. Ödev 10
1. Proje / Tasarım 20
Total 100
1. Final 50
1. Yıl İçinin Başarıya 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