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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts FREE CHAPTER 2. Learning Process in Neural Networks 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Supervised learning

Supervised learning is a learning method where there is a part of the training data which acts as a teacher to the algorithm to determine the model. The machine is taught what to learn from the target data. The target data, or dependent or response variables, are the outcome of the collective action of the independent variables. The network training is done with the target data and its behavior with patterns of input data. The target labels are known in advance and the data is fed to the algorithm to derive the model.

Most of neural network usage is done using supervised learning. The weights and biases are adjusted based on the output values. The output can be categorical (like true/false or 0/1/2) or continuous (like 1,2,3, and so on). The model is dependent on the type of output variables, and in the case of neural networks, the output layer is built on...

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