Welcome,
Guest
.
Login
.
Türkçe
NİNOVA
COURSES
HELP
ABOUT
Where Am I:
Ninova
/
Courses
/
Faculty of Computer and Informatics
/
YZV 302E
/
Course Informations
Return to Faculty
Home Page
Course Information
Course Weekly Lecture Plan
Course Evaluation Criteria
Course Information
Course Name
Turkish
Derin Öğrenme
English
Deep Learning
Course Code
YZV 302E
Credit
Lecture
(hour/week)
Recitation
(hour/week)
Laboratory
(hour/week)
Semester
5
3
3
-
-
Course Language
English
Course Coordinator
Gözde Ünal
Course Objectives
1. To introduce main techniques in Deep Learning
2. To understand the mathematical principles of optimization and regularization of deep learning methods
3. To be able to design deep neural networks for various problems in artificial intelligence
4. To implement solutions to learning problems using various deep neural network techniques
Course Description
Course contents: Deep Learning, Neural Networks and Convolutional Neural Networks, Optimization and Regularization, Supervised and Unsupervised Methods, Discriminative Networks, Training of Networks, Deep Generative Networks, Adversarial methods, Classification applications, Recurrent Neural Networks, Temporal Prediction applications, Attention Mechanisms, Advanced deep learning techniques and applications.
Course Outcomes
Students who successfully complete this course will be able to:
1. Know and discuss the main problems, application areas and the techniques of deep learning
2. Describe, construct and use necessary mathematical tools in deep learning such as optimization, regularization etc.
3. Design various types of convolutional, fully connected and sequential neural networks and construct and run necessary procedures for training of deep neural networks and evaluate the results
4. Know and apply different mathematical formulations and solution techniques to supervised and unsupervised approaches in learning, specifically discriminative and generative network models
5. Implement computer realizations of deep learning applications
Pre-requisite(s)
Basic Machine Learning. Probability Theory.
Required Facilities
Computer with GPU
Other
Textbook
Reference but not main textbook: Deep Learning, I. Goodfellow, Y. Bengio, A. Courville, 2016, MIT Press.
Other References
1. Deep Learning with Python, F. Chollet, 2017, Manning.
2. Fundamentals of Deep LearningDesigning Next-Generation MachineIntelligence Algorithms, Nikhil Buduma, Reilly
3. neuralnetworksanddeeplearning.com
Courses
.
Help
.
About
Ninova is an ITU Office of Information Technologies Product. © 2023