Neural networks with R :: smart models using CNN, RNN, deep learning, and artificial intelligence principles /
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of n...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788399418 1788399412 |
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| 520 | |a Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples. | ||
| 505 | 0 | |a Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles -- Credits -- About the Authors -- About the Reviewer -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Neural Network and Artificial Intelligence Concepts -- Chapter 2: Learning Process in Neural Networks -- Chapter 3: Deep Learning Using Multilayer Neural Networks -- Chapter 4: Perceptron Neural Network Modeling -- Basic Models -- Chapter 5: Training and Visualizing a Neural Network in R -- Chapter 6: Recurrent and Convolutional Neural Networks -- Chapter 7: Use Cases of Neural Networks -- Advanced Topics -- Index. | |
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| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
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| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
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| contents | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles -- Credits -- About the Authors -- About the Reviewer -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Neural Network and Artificial Intelligence Concepts -- Chapter 2: Learning Process in Neural Networks -- Chapter 3: Deep Learning Using Multilayer Neural Networks -- Chapter 4: Perceptron Neural Network Modeling -- Basic Models -- Chapter 5: Training and Visualizing a Neural Network in R -- Chapter 6: Recurrent and Convolutional Neural Networks -- Chapter 7: Use Cases of Neural Networks -- Advanced Topics -- Index. |
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| discipline | Informatik |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Ciaburro, Giuseppe, author. Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / Giuseppe Ciaburro, Balaji Venkateswaran. Birmingham, UK : Packt Publishing, 2017. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed October 31, 2017). Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A step-by-step guide filled with real-world practical examples. Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles -- Credits -- About the Authors -- About the Reviewer -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Neural Network and Artificial Intelligence Concepts -- Chapter 2: Learning Process in Neural Networks -- Chapter 3: Deep Learning Using Multilayer Neural Networks -- Chapter 4: Perceptron Neural Network Modeling -- Basic Models -- Chapter 5: Training and Visualizing a Neural Network in R -- Chapter 6: Recurrent and Convolutional Neural Networks -- Chapter 7: Use Cases of Neural Networks -- Advanced Topics -- Index. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 R (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat COMPUTERS Neural Networks. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast R (Computer program language) fast Venkateswaran, Balaji, author. has work: Neural networks with R (Text) https://id.oclc.org/worldcat/entity/E39PCGMTGMbkb9HwPVwTFMYv6q https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1607842 Volltext |
| spellingShingle | Ciaburro, Giuseppe Venkateswaran, Balaji Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles -- Credits -- About the Authors -- About the Reviewer -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Neural Network and Artificial Intelligence Concepts -- Chapter 2: Learning Process in Neural Networks -- Chapter 3: Deep Learning Using Multilayer Neural Networks -- Chapter 4: Perceptron Neural Network Modeling -- Basic Models -- Chapter 5: Training and Visualizing a Neural Network in R -- Chapter 6: Recurrent and Convolutional Neural Networks -- Chapter 7: Use Cases of Neural Networks -- Advanced Topics -- Index. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 R (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat COMPUTERS Neural Networks. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh90001937 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D016571 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / |
| title_auth | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / |
| title_exact_search | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / |
| title_full | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / Giuseppe Ciaburro, Balaji Venkateswaran. |
| title_fullStr | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / Giuseppe Ciaburro, Balaji Venkateswaran. |
| title_full_unstemmed | Neural networks with R : smart models using CNN, RNN, deep learning, and artificial intelligence principles / Giuseppe Ciaburro, Balaji Venkateswaran. |
| title_short | Neural networks with R : |
| title_sort | neural networks with r smart models using cnn rnn deep learning and artificial intelligence principles |
| title_sub | smart models using CNN, RNN, deep learning, and artificial intelligence principles / |
| topic | R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Neural networks (Computer science) http://id.loc.gov/authorities/subjects/sh90001937 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Neural Networks, Computer https://id.nlm.nih.gov/mesh/D016571 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 R (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. aat COMPUTERS Neural Networks. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Information Technology. bisacsh COMPUTERS General. bisacsh Artificial intelligence fast Machine learning fast Neural networks (Computer science) fast R (Computer program language) fast |
| topic_facet | R (Computer program language) Neural networks (Computer science) Machine learning. Artificial intelligence. Neural Networks, Computer Artificial Intelligence Machine Learning R (Langage de programmation) Réseaux neuronaux (Informatique) Apprentissage automatique. Intelligence artificielle. artificial intelligence. COMPUTERS Neural Networks. COMPUTERS Intelligence (AI) & Semantics. COMPUTERS Information Technology. COMPUTERS General. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1607842 |
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