This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You'll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics.

By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects.Use R 3.5 for building deep learning models for computer vision and text.

## Book Details | |

Publisher: | Packt Publishing |

By: | Mark Hodnett, Joshua F. Wiley |

ISBN-13: | 9781788992893 |

ISBN-10: | 178899289X |

Year: | 2018 |

Pages: | 378 |

Language: | English |

## Book Preview | |

Online | R Deep Learning Essentials, 2nd Edition |

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Source Code | R Deep Learning Essentials, 2nd Edition |

## Paper Book | |

Buy: | R Deep Learning Essentials, 2nd Edition |

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