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MATLAB deep learning : with machine learning, neural networks and artificial intelligence /
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MA...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[New York, NY] :
Apress,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Kim, Phil, |e author. | |
| 245 | 1 | 0 | |a MATLAB deep learning : |b with machine learning, neural networks and artificial intelligence / |c Phil Kim. |
| 264 | 1 | |a [New York, NY] : |b Apress, |c 2017. | |
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| 520 | |a Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. | ||
| 588 | 0 | |a Vendor-supplied metadata. | |
| 505 | 0 | |a At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learning?; Challenges with Machine Learning; Overfitting; Confronting Overfitting; Types of Machine Learning; Classification and Regression; Summary; Chapter 2: Neural Network; Nodes of a Neural Network; Layers of Neural Network; Supervised Learning of a Neural Network; Training of a Single-Layer Neural Network: Delta Rule; Generalized Delta Rule; SGD, Batch, and Mini Batch; Stochastic Gradient Descent; Batch; Mini Batch. | |
| 505 | 8 | |a Example: Delta RuleImplementation of the SGD Method; Implementation of the Batch Method; Comparison of the SGD and the Batch; Limitations of Single-Layer Neural Networks; Summary; Chapter 3: Training of Multi-Layer Neural Network; Back-Propagation Algorithm; Example: Back-Propagation; XOR Problem; Momentum; Cost Function and Learning Rule; Example: Cross Entropy Function; Cross Entropy Function; Comparison of Cost Functions; Summary; Chapter 4: Neural Network and Classification; Binary Classification; Multiclass Classification; Example: Multiclass Classification; Summary. | |
| 505 | 8 | |a Chapter 5: Deep LearningImprovement of the Deep Neural Network; Vanishing Gradient; Overfitting; Computational Load; Example: ReLU and Dropout; ReLU Function; Dropout; Summary; Chapter 6: Convolutional Neural Network; Architecture of ConvNet; Convolution Layer; Pooling Layer; Example: MNIST; Summary; Index. | |
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a MATLAB. |
| 630 | 0 | 7 | |a MATLAB |2 fast |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 2 | |a Neural Networks, Computer | |
| 650 | 2 | |a Machine Learning | |
| 650 | 1 | 4 | |a Computer Science. |
| 650 | 2 | 4 | |a Big Data. |
| 650 | 2 | 4 | |a Artificial Intelligence (incl. Robotics). |
| 650 | 2 | 4 | |a Mathematical Logic and Formal Languages. |
| 650 | 2 | 4 | |a Programming Languages, Compilers, Interpreters. |
| 650 | 2 | 4 | |a Programming Techniques. |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Mathematical theory of computation. |2 bicssc | |
| 650 | 7 | |a Programming & scripting languages: general. |2 bicssc | |
| 650 | 7 | |a Computer programming / software development. |2 bicssc | |
| 650 | 7 | |a Databases. |2 bicssc | |
| 650 | 7 | |a MATHEMATICS |x General. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast | |
| 776 | 0 | 8 | |i Printed edition: |z 9781484228449 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781484228456/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 880 | 4 | |6 264-00 |c �2017 | |
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