SECTION I: GENERAL INFORMATION ABOUT THE COURSE |
Course Code | Course Name | Year | Semester | Theoretical | Practical | Credit | ECTS |
60610METOS-YZM0389 | Deep Learning | 4 | Spring | 2 | 2 | 3 | 5 |
Course Type : | Elective Course IV |
Cycle: | Bachelor TQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree |
Language of Instruction: | Turkish |
Prerequisities and Co-requisities: | N/A |
Mode of Delivery: | Face to face |
Name of Coordinator: | Öğretim Görevlisi Dr. ENVER AKBACAK |
Dersin Öğretim Eleman(lar)ı: |
Öğretim Görevlisi Dr. ENVER AKBACAK |
Dersin Kategorisi: |
SECTION II: INTRODUCTION TO THE COURSE |
Course Objectives: | 1. To familiarize students with Deep Learning Fundamentals. 2. To Introduce concepts for image, video and text recognition. 3. To teach students featured deep learning practices by a project. Textbook: http://neuralnetworksanddeeplearning.com/ and Deep Learning with Python (François Chollet) Software Tensorflow’s Keras Tools: Google’s Colab A long-term project will be assigned to students. The project reports will be prepared on any word processor in the following format. Midterm Exam 20% Final Exam 50% Projects 30% |
Course Content: | Fundamentals What is deep learning, layers, learning, wieghts, loss functions, optimizers. Neural Networks Perceptrons, sigmoid neurons, learning with gradient descent and momentum based gradient descent. Neural Networks Back propagation. Cost Functions, cross entropy, softmax, regularization, wieght initialization, early stopping, batch size, tanh activation, dropout, normalization, relu layers, pooling layers. Mathematical fundamentals of Neural Networks Getting Started with Neural Networks Fundamentals of Deep Learning Supervised, unsupervised, selfsupervised and reinforcement learnings, train/validation/test splits allocations, vanishing gradient descent problem, overfitting and underfitting. Convolutional Neural Networks Fundamentals Pretrained models Fine-tuning Convolutional Neural Networks Feature extraction Python generators Visualizing intermediate activations and filters Class activation maps Encoder – Decoder Models Recurrent Neural Networks LSTMs, Combining CNN-LSTM models Video Processing 3D CNN, Keras functional API, callbacks Project Presentations |
Projects A long-term project will be assigned to students. The project reports will be prepared on any word processor in the following format. Page 1. • Project title • Student's name • Date due • Abstract (min 1/2 page) Page 2. Technical presentation. This section should include implemented technics and equations. (One page) Page 3 Discussion of findings. Significant findings in terms of the project objectives and reference any images generated. (Min two pages) Results. Includes all the images generated in the project. Number images individually so they can be referenced in the preceding section. Appendix. Computer codes with explanations written by the student. Projects not conform to the requested format may be grounds for rejection. |
Knowledge (Described as Theoritical and/or Factual Knowledge.) | ||
Skills (Describe as Cognitive and/or Practical Skills.) | ||
Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.) |
Week | Subject | ||
Related Preparation | Further Study | ||
1) | Fundamentals What is deep learning, layers, learning, wieghts, loss functions, optimizers. | ||
2) | Neural Networks Perceptrons, sigmoid neurons, learning with gradient descent and momentum based gradient descent. | ||
3) | Neural Networks Back propagation. | ||
4) | Neural Networks Cost Functions, cross entropy, softmax, regularization, wieght initialization, early stopping, batch size, tanh activation, dropout, normalization, relu layers, pooling layers. | ||
5) | Mathematical fundamentals of Neural Networks | ||
6) | Getting Started with Neural Networks | ||
7) | Fundamentals of Deep Learning Supervised, unsupervised, selfsupervised and reinforcement learnings, train/validation/test splits allocations, vanishing gradient descent problem, overfitting and underfitting. | ||
8) | Convolutional Neural Networks Fundamentals Pretrained models Fine-tuning | ||
9) | Convolutional Neural Networks Feature extraction Python generators Visualizing intermediate activations and filters Class activation maps | ||
10) | Encoder – Decoder Models | ||
11) | Recurrent Neural Networks LSTMs, Combining CNN-LSTM models | ||
12) | Video Processing 3D CNN, Keras functional API, callbacks | ||
13) | Project Presentations | ||
14) | Project Presentations |
Course Notes / Textbooks: | Hafta Konular Ders Kitabı Değerlendirme ve İlave Bilgiler 1 Derin Öğrenmeye Giriş Derin öğrenme temel kavramlar, Gerekli Matematiksel Tanımlamalar, Uygulamalarda kullanılacak arayüz ve yazılımların tanıtım ve kurulum uygulamalarını içermektedir. A 2 Yapay Sinir Ağları Temel Kavramlar Logistik Regresyon(LR), LR Cost Fonksiyonu, LR Gradient Descent, LR türev (derivatives) kavramlarını ve uygulamalarını içermektedir. A 3 Yapay Sinir Ağları (ANN) ANN aktivasyon fonksiyonları ve türev(derivative) hesaplamalarını ve ANN derin ağların oluşturulması içermektedir. A Projelerin Atanması 4 Yapay Sinir Ağları(ANN) ANN İleri-Geri Yayılım (Forward-Backward Propagation) tanıtım ve uygulamalarını içermektedir. A-B 5 Derin Öğrenme Stratejileri Derin Öğrenmede Bias Variance Kavramlarını, Performans Metriklerini ve Veri setlerinin Eğitim-Validasyon-Test Kümelerine ayrılma stratejilerini içermektedir. A-B 6 Derin Öğrenme Optimizasyon Parameterleri Regularization, Dropout, Normalization, Vanishing Gradient kavramlarını ayrıca Gradient Descent, Adam ve Rmsprop Optimizasyon Algoritmaları ile Learning Rate tanımlamaları ile Uygulamalarını içermektedir. A-B 7 Evrişimsel Sinir Ağları(CNN) Temelleri CNN matematiksel tanımlamalarını, Padding, Stride tanımlamaları ile çıkış haritalarının hesaplamalarını ayrıca temel CNN yapısı ve uygulamalarını içermektedir. B İlk Proje İncelemeleri 8 VIZE SINAVI 9 Evrişimsel Sinir Ağları(CNN) Modelleri-1 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) ‘de başarı sağlayan Alexnet VGG-16, VGG-19 model yapılarının incelenmesini ve Uygulamalarını içermektedir. B 10 Evrişimsel Sinir Ağları(CNN) Modelleri-2 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) ‘de başarı sağlayan Resnet, Inception Networks, Mobilenet model yapılarının incelenmesini ve Uygulamalarını içermektedir. B 11 Encoder – Decoder Modeller Autoencoder- Decoder modeller ve U-NET yapılarının incelenmesini ve Uygulamalarını içermektedir. PROJE SUNUMLARI-1 B İkinci Proje İncelemeleri 12 Tekrarlayan Sinir Ağları (RNN) Long Short-Term Memory (LSTM) Kavramları, CNN-LSTM Uygulamalarını içermektedir. PROJE SUNUMLARI-2 A-B İkinci Proje İncelemeleri 13 Evrişimli Sinir Ağları ile Nesne Tespiti YOLO ile nesne tespiti için tanımlamalar ve uygulamalarını içermektedir. PROJE SUNUMLARI-3 A-B İkinci Proje İncelemeleri 14 Final Sınavı |
References: | Textbook: http://neuralnetworksanddeeplearning.com/ Deep Learning with Python (François Chollet) Software Tensorflow’s Keras Tools: Google’s Colab |
SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs) |
CLOs/PLOs | KPLO 1 | KPLO 2 | KPLO 3 | KPLO 4 | KPLO 5 | ||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE |
Lectures | |
Discussion | |
Case Study | |
Problem Solving | |
Demonstration | |
Views | |
Laboratory | |
Reading | |
Homework | |
Project Preparation | |
Thesis Preparation | |
Peer Education | |
Seminar | |
Technical Visit | |
Course Conference | |
Brain Storming | |
Questions Answers | |
Individual and Group Work | |
Role Playing-Animation-Improvisation | |
Active Participation in Class |
Midterm | |
Presentation | |
Final Exam | |
Quiz | |
Report Evaluation | |
Homework Evaluation | |
Oral Exam | |
Thesis Defense | |
Jury Evaluation | |
Practice Exam | |
Evaluation of Implementation Training in the Workplace | |
Active Participation in Class | |
Participation in Discussions |
LEARNING & TEACHING METHODS | ASSESMENT & EVALUATION METHODS | ||||||||||||||||||||
-Lectures | -Midterm | ||||||||||||||||||||
-Discussion | -Presentation | ||||||||||||||||||||
-Case Study | -Final Exam | ||||||||||||||||||||
-Problem Solving | -Quiz | ||||||||||||||||||||
-Demonstration | -Report Evaluation | ||||||||||||||||||||
-Views | -Homework Evaluation | ||||||||||||||||||||
-Laboratory | -Oral Exam | ||||||||||||||||||||
-Reading | -Thesis Defense | ||||||||||||||||||||
-Homework | -Jury Evaluation | ||||||||||||||||||||
-Project Preparation | -Practice Exam | ||||||||||||||||||||
-Thesis Preparation | -Evaluation of Implementation Training in the Workplace | ||||||||||||||||||||
-Peer Education | -Active Participation in Class | ||||||||||||||||||||
-Seminar | - Participation in Discussions | ||||||||||||||||||||
-Technical Visit | |||||||||||||||||||||
-Course Conference | |||||||||||||||||||||
-Brain Storming | |||||||||||||||||||||
-Questions Answers | |||||||||||||||||||||
-Individual and Group Work | |||||||||||||||||||||
-Role Playing-Animation-Improvisation | |||||||||||||||||||||
-Active Participation in Class |
Measurement and Evaluation Methods | # of practice per semester | Level of Contribution |
Project | 2 | % 30.00 |
Midterms | 1 | % 20.00 |
Semester Final Exam | 1 | % 50.00 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 50 | |
PERCENTAGE OF FINAL WORK | % 50 | |
Total | % 100 |
SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE |
WORKLOAD OF TEACHING & LEARNING ACTIVITIES | |||
Teaching & Learning Activities | # of Activities per semester | Duration (hour) | Total Workload |
Course | 0 | 0 | 0 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 0 | 0 | 0 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 0 | 0 | 0 |
Homework Assignments | 0 | 0 | 0 |
Total Workload of Teaching & Learning Activities | - | - | 0 |
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES | |||
Assesment & Evaluation Activities | # of Activities per semester | Duration (hour) | Total Workload |
Quizzes | 0 | 0 | 0 |
Midterms | 0 | 0 | 0 |
Semester Final Exam | 0 | 0 | 0 |
Total Workload of Assesment & Evaluation Activities | - | - | 0 |
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) | 0 | ||
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) | 5 |