Stanimirović, Ivan, author.
Deep neural networks and applications / Ivan Stanimirović. - Oakville, Ontario : Arcler Press, c2020. - xv, 181 pages : color illustrations
To access the E-Book : https://www.bibliotex.com/ (Log-in/Register is required).
Includes Bibliography references and index.
Chapter 1 : Introducion to Artificial Neural Networks (ANN)
Chapter 2 : Main Neural Network Topologies
Chapter 3 : Artificial Intelligence and its Application in Risk Management
Chapter 4 : Artificial Neural Networks
Chapter 5 : Deep Neural Networks Applied in Practice
Chapter 6 : Optimizing Deep Learning and Ensemble Models for Best Performance on E-Learning Data
Chapter 7 : Methodology and The Research Framework
Chapter 8 : Developing Algorithms that Speed Up Data Processing Over Complex Network Architectures to train a Machine Learning Model
Chapter 9 : Optimization of Database Query Structures Using Deep Learning
Chapter 10 : Conclusion
Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial Neutral Networks in detail and discusses the practical applications of the deep neutral networks. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research framework, development of the algorithms that quicken the data processing over complex network architectures and the optimization of database query structures using deep learning
In English text.
Neural networks (Computer science).
Machine learning.
EBOP QA 76.87 / S73 2020
Deep neural networks and applications / Ivan Stanimirović. - Oakville, Ontario : Arcler Press, c2020. - xv, 181 pages : color illustrations
To access the E-Book : https://www.bibliotex.com/ (Log-in/Register is required).
Includes Bibliography references and index.
Chapter 1 : Introducion to Artificial Neural Networks (ANN)
Chapter 2 : Main Neural Network Topologies
Chapter 3 : Artificial Intelligence and its Application in Risk Management
Chapter 4 : Artificial Neural Networks
Chapter 5 : Deep Neural Networks Applied in Practice
Chapter 6 : Optimizing Deep Learning and Ensemble Models for Best Performance on E-Learning Data
Chapter 7 : Methodology and The Research Framework
Chapter 8 : Developing Algorithms that Speed Up Data Processing Over Complex Network Architectures to train a Machine Learning Model
Chapter 9 : Optimization of Database Query Structures Using Deep Learning
Chapter 10 : Conclusion
Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial Neutral Networks in detail and discusses the practical applications of the deep neutral networks. Also discussed in the book is the optimization of deep learning for the best performance of e-learning data, the methodology and the research framework, development of the algorithms that quicken the data processing over complex network architectures and the optimization of database query structures using deep learning
In English text.
Neural networks (Computer science).
Machine learning.
EBOP QA 76.87 / S73 2020