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Week
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Topics
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Study Metarials
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1
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Introduction to Deep Learning
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R1-Part-1
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2
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Deep Learning History
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R1-Part-2
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3
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Deep Learning Architectures
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R1-Part-2
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4
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Deep Learning Libraries
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R1-Part-2
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5
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Deep Learning Algorithms : Deep Boltzmann Machine (DBM) & Deep Belief Networks (DBN)
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R1-Part-5
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6
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Deep Learning Algorithms : Recurrent Neural Networks (RNNs) & Long Short-Term Memory Networks (LSTMs)
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R1-Part-5
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7
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Deep Learning Algorithms : Convolutional Neural Network (CNN)
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R1-Part-5
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8
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Creating Deep Neural Network Model: Definition
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R1-Part-10
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9
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Creating Deep Neural Network Model: Image Pre-Processing
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R1-Part-10
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10
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Creating Deep Neural Network Model: Model Parameters
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R1-Part-10
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11
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Hyper-Parameters
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R1-Part-10
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12
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Pooling, Optimization and Drop-Out
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R1-Part-10
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13
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Classification
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R1-Part-10
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14
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Data Visualization
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R1-Part-10
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Prerequisites
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-
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Language of Instruction
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Turkish
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Responsible
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Assist. Prof. Dr. Fuat TÜRK
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Instructors
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1-)Doçent Dr. Selim Buyrukoğlu
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Assistants
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-
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Resources
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R1- Deng, L., & Yu, D. (2014). Deep Learning: Methods and Applications. Foundations and Trends in Signal Processing, 7(3-4), 197-387.
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Supplementary Book
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-
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Goals
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To introduce students to the basic concepts and techniques of Deep Learning.
To develop skills of using recent deep learning algorithms for solving practical problems.
To learn creating deep neural network structure.
To gain experience of doing independent study and research.
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Content
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Deep learning history, architectures, libraries, Pre-defined deep learning models, Creating deep learning model, Image pre-processing, Hyper-parameters, Classification processes, Data visualization.
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Program Learning Outcomes |
Level of Contribution |
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1
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Acquires information by carrying out scientific research in the field of Electronics and Computer Engineering, evaluates the findings and makes comments
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2
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2
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Complements the restricted or incomplete information and applies it, unifies the multidisciplinary information
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-
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3
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Designs and implements a system meeting the requirements in the field of Electronics and Computer Engineering
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3
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4
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Makes an interpretation of a problem in the field of Electronics and Computer Engineering, develops models for solutions and applies innovative methods in these solutions
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-
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5
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Has comprehensive knowledge on the contemporary applied method and techniques used in the field of Electronics and Computer Engineering and their limitations
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-
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6
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Undertakes and implements analytic, simulation or experimental types of research and has the ability to solve the complex problems encountered there
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4
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7
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Can participate and assume responsibility in multidisciplinary task forces
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-
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8
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Observes the scientific, professional and ethical rules during data collection, its introduction and interpretation
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4
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9
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Be aware of recent advances and developments in the field of Electronics and Computer Engineering learns, analyses and applies them wherever needed
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-
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10
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Publishes his/her research findings verbally and in written forms in the national and international arena
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-
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