Deep Learning and Deep Learning Models Used in Image Analysis

Abstract

In order to establish a  machine learning system or model definition with classical machine learning techniques, it is necessary to first extract the feature vector. Experts are needed for extract the feature vector. For this reason, these techniques are insufficient at the point where a raw data can be processed. Deep learning has made tremendous progress by eliminating this problem, which has been a challenge for many years in the field of machine learning. Unlike traditional machine learning and image processing techniques, Deep Learning do the learning process on raw data. It obtains the necessary information from the representations that it formed in different layers. Deep learning uses many areas such as image recognition, voice recognition, natural language processing and gene analysis etc. Deep learning first attracted attention with its success in the Large Scale Visual Recognition (ImageNet) competition for object classification in 2012. In fact, the foundations of Deep Learning depend on the past. But it has become popular in recent years mainly due to two reasons. The first is the existence of as much data as training. The second is the hardware infrastructure that will process this data. In this study, information about deep learning was given and detailed information about layers of convolution, pooling, ReLu and fully connected layers, which are layers of Convolution Neural Network (CNN) architecture. It also describes AlexNet, ZFNet, GoogLeNet, Microsoft RestNet and Region with Convolution Neural Network (R-CNN) architectures, which can be considered as basic architects for Deep Learning.

Keywords

• Deep Learning,CNN,Convolution,Pooling,AlexNet,ZFNet,GoogLeNet,Microsoft RestNet,R-CNN

Derin Öğrenme ve Görüntü Analizinde Kullanılan Derin Öğrenme Modelleri

Abstract

Klasik Makine öğrenme teknikleri ile bir model tanımlama veya makine öğrenimi sistemi kurmak için öncelikle özellik vektörünün çıkarılması gerekmektedir. Özellik vektörünün çıkarılması için alanında uzman kişilere ihtiyaç duyulmaktadır. Bu işlemler hem çok zaman almakta hem de uzmanı çok meşgul etmektedir. Bu sebeple bu teknikler, ham bir veriyi ön işlem yapmadan ve uzman yardımı olmadan işleyemezler. Derin Öğrenme makine öğrenimi alanında çalışanların uzun yıllar boyunca uğraştığı bu sorunu ortadan kaldırarak büyük ilerleme sağlamıştır. Çünkü derin ağlar geleneksel makine öğrenmesi ve görüntü işleme tekniklerinin aksine öğrenme işlemini ham veri üzerinde yapmaktadır. Ham veriyi işlerken gerekli bilgiyi farklı katmanlarda oluşturmuş olduğu temsillerle elde etmektedir. Derin Öğrenme ilk defa 2012 yılında nesne sınıflandırma için yapılan, büyük ölçekli görsel tanıma (ImageNet) yarışmasında elde ettiği başarı ile dikkatleri üzerine çekmiştir. Derin Öğrenmenin temelleri geçmişe dayansa da özellikle son yıllarda popüler olmasının en önemli sebeplerinden ilki eğitim için yeteri kadar verinin olması ve ikinci olarak bu veriyi işleyecek donanımsal alt yapının olmasıdır. Bu çalışmada Derin Öğrenme hakkında detaylı bilgi verilmiştir. Evrişimsel Sinir Ağı(ESA) mimarisinin katmanları olan Konvolüsyon, Havuzlama, ReLu, DropOut, Tam bağlantılı ve Sınıflandırma katmanı hakkında açıklamalar yapılmıştır. Ayrıca Derin Öğrenmede temel mimariler olarak kabul edilebilecek AlexNet, ZFNet, GoogLeNet, Microsoft RestNet ve R-CNN mimarileri anlatılmıştır.

Keywords

• Derin Öğrenme,CNN,Konvolüsyon,Pooling,AlexNet,ZFNet,GoogLeNet,Microsoft RestNet,R-CNN

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