Abstract:
Deep learning become a popular trend in current research and applications. Deep neural networks are important part of this trend. The paper shows the effect of neural net...Show MoreMetadata
Abstract:
Deep learning become a popular trend in current research and applications. Deep neural networks are important part of this trend. The paper shows the effect of neural network architecture on its power and capacity for solving complex, nonlinear problems. The problem has been analyzed using trigonometric, polynomial and digital approaches. Presented analysis show that the network capacity increases linearly with the network width and increases exponentially with the network depth.
Date of Conference: 21-23 October 2018
Date Added to IEEE Xplore: 30 December 2018
ISBN Information:
ISSN Information:
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Deep Network ,
- Deep Neural Network ,
- Exponential Growth ,
- Nonlinear Problem ,
- Network Depth ,
- Digital Approach ,
- Current Research Trends ,
- Activation Function ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Nonlinear Function ,
- Single Layer ,
- Hidden Layer ,
- Recurrent Neural Network ,
- Neurons In Layer ,
- Order Polynomial ,
- Deep Architecture ,
- Similar Considerations ,
- Neurons In The Hidden Layer ,
- Shallow Network ,
- Shallow Neural Network ,
- Neuron Activation Function ,
- Restricted Boltzmann Machine ,
- Deep Belief Network ,
- Successful Training ,
- Vanishing Gradient
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Neural Network ,
- Deep Network ,
- Deep Neural Network ,
- Exponential Growth ,
- Nonlinear Problem ,
- Network Depth ,
- Digital Approach ,
- Current Research Trends ,
- Activation Function ,
- Convolutional Neural Network ,
- Support Vector Machine ,
- Nonlinear Function ,
- Single Layer ,
- Hidden Layer ,
- Recurrent Neural Network ,
- Neurons In Layer ,
- Order Polynomial ,
- Deep Architecture ,
- Similar Considerations ,
- Neurons In The Hidden Layer ,
- Shallow Network ,
- Shallow Neural Network ,
- Neuron Activation Function ,
- Restricted Boltzmann Machine ,
- Deep Belief Network ,
- Successful Training ,
- Vanishing Gradient
- Author Keywords