Literature Review of Deep Learning Research Areas

Year 2019, Volume: 5 Issue: 3, 188 - 215, 30.12.2019

M. Mutlu Yapıcı , Adem Tekerek , Nurettin Topaloğlu

https://doi.org/10.30855/gmbd.2019.03.01

Abstract

Deep learning
(DL) is a powerful machine learning field that has achieved considerable
success in many research areas. Especially in the last decade,
the-state-of-the-art studies on many research areas such as computer vision,
object recognition, speech recognition, natural language processing were led to
the awakening of the artificial intelligence from deep sleep. Nowadays, many
researchers are trying to find solutions to many problems in various fields
under the light of DL methods. In this study, we present important knowledge to
guide about DL models and challenging topics which can be used in DL for
researchers. We investigated DL studies which are made in the most popular and
challenging fields such as Autonomous Vehicles, Natural Language Processing, Handwritten
Character Recognition, Signature Verification, Voice and Video Recognition,
Medical İmage Processing, Big Data. Furthermore, we point out the remaining
challenges of these research areas these can be solved by DL and discuss the
future topics in order to help the researchers. The contribution of this study
is that to list the most challenging subjects that can be studied with DL. We
believe that researchers will contribute to these issues by achieving
successful results through DL algorithms. The goal of this work is to help them
make informed decisions about the best DL model that fits the needs and
resources of researchers seeking to work with DL.

Keywords

Deep learning, Convolutional Neural networks, Recurrent Neural Networks

Derin Öğrenme Araştırma Alanlarının Literatür Taraması

Abstract

Derin öğrenme
(Deep Learning-DL), birçok alanda önemli başarılar elde etmiş güçlü bir makine
öğrenmesi yöntemidir. Özellikle son on yılda, bilgisayarlı görü, nesne tanıma,
konuşma tanıma, doğal dil işleme gibi birçok araştırma alanında başarılı
sonuçlar elde ederek, yapay zekanın derin uykudan uyanmasına yol açmıştır.
Günümüzde, çeşitli alanlardaki birçok araştırmacı, DL yöntemlerini kullanarak
alanlarında en iyi sonucu almaya çalışmaktadır. Bu tarama çalışmasında, DL
modelleri ve DL ile çalışılabilecek önemli araştırma konuları hakkında  bilgiler vererek araştırmacılara rehberlik
etmeyi hedefliyoruz. Çalışmada Özerk Araçlar (Autonomous Vehicles), Doğal Dil
İşleme (Natural Language Processing), El Yazısı Karakter Tanıma (Handwritten
Character Recognition), İmza Doğrulama (Signature Verification), Ses ve Video
Tanıma (Voice and Video Recognition), Tıbbi Görüntü İşleme (Medical İmage
Processing), Büyük Veri (Big Data) gibi dünyanın en popüler ve en zorlu
alanlarında yapılan DL çalışmalarını inceliyoruz. Ayrıca, araştırmacılara
yardımcı olmak için, incelediğimiz bu alanlardaki DL ile çalışılabilecek, henüz
çalışılmamış veya yeterince iyi sonuçlar elde edilememiş problemlere dikkat
çekerek olası araştırma konularını listeliyoruz. Bu çalışmanın nihai amacı, DL
ile çalışmak isteyen araştırmacılara umut vadeden yeni konuları gösterebilmek
ve, araştırmacıların ihtiyaçlarına uyan en iyi DL modelini seçebilmeleri için
modeller hakkında bilinçli kararlar vermelerine yardımcı olmaktır.

Keywords

Derin Öğrenme, Evrişimsel Sinir Ağları, Tekrarlayan Sinir Ağları

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