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Advanced Deep Learning with R

Advanced Deep Learning with R

By : Rai
4.3 (3)
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Advanced Deep Learning with R

Advanced Deep Learning with R

4.3 (3)
By: Rai

Overview of this book

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.
Table of Contents (20 chapters)
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1
Section 1: Revisiting Deep Learning Basics
3
Section 2: Deep Learning for Prediction and Classification
6
Section 3: Deep Learning for Computer Vision
12
Section 4: Deep Learning for Natural Language Processing
17
Section 5: The Road Ahead

Summary

Deep learning methods that make use of artificial neural networks have been increasing in popularity in recent years. A number of areas of application involving deep learning methods include driverless cars, image classification, natural language processing, and new image generation. We started this first chapter by looking at the popularity of the deep learning term as reported from a Google trend website. We described a general five-step process for applying deep learning methods and developed some broad ideas about details within each step. We then briefly looked at deep learning techniques covered in each chapter and situations in which they are applied, along with some best practices.

In the next chapter, we get started with an application example and illustrate steps for developing a deep network model for multi-class classification problems.

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