Abstract
Deep Learning is a promising field of Artificial Intelligence algorithms that have proven to be capable of solving a wide range of tasks including classification, object detection, regression, face recognition, augmented and virtual reality, self-driving cars and many more. This chapter introduces the reader to Deep Learning, its basic principles, and applications. It covers the essential elements of any Deep Learning system, as well as explains how to connect these elements to form a neural network. The reader will understand the reasoning behind the Deep Learning and why it is so useful nowadays. The training algorithm of the neural network is also covered in this chapter.
| Original language | English |
|---|---|
| Title of host publication | Modeling and Optimization in Science and Technologies |
| Publisher | Springer Verlag |
| Pages | 41-55 |
| Number of pages | 15 |
| DOIs | |
| Publication status | Published - Jan 1 2020 |
Publication series
| Name | Modeling and Optimization in Science and Technologies |
|---|---|
| Volume | 14 |
| ISSN (Print) | 2196-7326 |
| ISSN (Electronic) | 2196-7334 |
Fingerprint
ASJC Scopus subject areas
- Modelling and Simulation
- Medical Assisting and Transcription
- Applied Mathematics
Cite this
Deep learning theory simplified. / Bakambekova, Adilya; James Pappachen, Alex.
Modeling and Optimization in Science and Technologies. Springer Verlag, 2020. p. 41-55 (Modeling and Optimization in Science and Technologies; Vol. 14).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Deep learning theory simplified
AU - Bakambekova, Adilya
AU - James Pappachen, Alex
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Deep Learning is a promising field of Artificial Intelligence algorithms that have proven to be capable of solving a wide range of tasks including classification, object detection, regression, face recognition, augmented and virtual reality, self-driving cars and many more. This chapter introduces the reader to Deep Learning, its basic principles, and applications. It covers the essential elements of any Deep Learning system, as well as explains how to connect these elements to form a neural network. The reader will understand the reasoning behind the Deep Learning and why it is so useful nowadays. The training algorithm of the neural network is also covered in this chapter.
AB - Deep Learning is a promising field of Artificial Intelligence algorithms that have proven to be capable of solving a wide range of tasks including classification, object detection, regression, face recognition, augmented and virtual reality, self-driving cars and many more. This chapter introduces the reader to Deep Learning, its basic principles, and applications. It covers the essential elements of any Deep Learning system, as well as explains how to connect these elements to form a neural network. The reader will understand the reasoning behind the Deep Learning and why it is so useful nowadays. The training algorithm of the neural network is also covered in this chapter.
UR - http://www.scopus.com/inward/record.url?scp=85064768925&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064768925&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-14524-8_3
DO - 10.1007/978-3-030-14524-8_3
M3 - Chapter
AN - SCOPUS:85064768925
T3 - Modeling and Optimization in Science and Technologies
SP - 41
EP - 55
BT - Modeling and Optimization in Science and Technologies
PB - Springer Verlag
ER -