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Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
INTRODUCTION TO DEEP LEARNING ENF 547 0 3 + 0 3 6
Ön Koşul Dersleri
Önerilen Seçmeli Dersler
Dersin Dili Türkçe
Dersin Seviyesi Yüksek Lisans
Dersin Türü SECMELI
Dersin Koordinatörü Doç.Dr. DEVRİM AKGÜN
Dersi Verenler
Dersin Yardımcıları
Dersin Kategorisi Alanına Uygun Öğretim
Dersin Amacı

To teach mathematical fundamentals about deep learning, to use open source libraries related to deep learning, to develop deep learning applications.

Dersin İçeriği

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

Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri
1 - Knows the basic topics about deep learning 1 - 2 - 4 - 15 - 16 - A - C - D -
2 - Knows 1D and 2D convnet-convolution layers. 1 - 2 - 4 - 15 - 16 - A - C - D -
3 - Uses open source libraries for deep learning 1 - 2 - 4 - 15 - 16 - A - C - D -
4 - Knows text processing, Embedding Layer, Simple RNN, LTSM and GRU layers 1 - 4 - 15 - 16 - A - C - D -
Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 4:Drilland Practice 15:Problem Solving 16:Project Based Learning
Ölçme Yöntemleri: A:Testing C:Homework D:Project / Design

Ders Akışı

Hafta Konular ÖnHazırlık
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

Kaynaklar

Ders Notu

Weekly slides

Ders Kaynakları

Goodfellow, Ian, et al. Deep learning. Vol. 1. Cambridge: MIT press, 2016.

Chollet, Francois. Deep learning with python. Manning Publications Co., 2017.


Döküman Paylaşımı


Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 X
2 X
3 X
4 X
5 X
6 X
7 X
8 X

Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 60
ProjeTasarim 1 20
Odev 1 10
Odev 2 10
Toplam 100
Yıliçinin Başarıya Oranı 50
Finalin Başarıya Oranı 50
Toplam 100

AKTS - İş Yükü

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