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Revisiting Deep Learning Architecture and Techniques

Deep learning is part of a broader machine learning and artificial intelligence field that uses artificial neural networks. One of the main advantages of deep learning methods is that they help to capture complex relationships and patterns contained in data. When the relationships and patterns are not very complex, traditional machine learning methods may work well. However, with the availability of technologies that help to generate and process more and more unstructured data, such as images, text, and videos, deep learning methods have become increasingly popular as they are almost a default choice to deal with such data. Computer vision and natural language processing (NLP) are two areas that are seeing interesting applications in a wide variety of fields, such as driverless cars, language translation, computer games, and...

Key benefits

Implement deep learning algorithms to build AI models with the help of tips and tricks

Understand how deep learning models operate using expert techniques

Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets

Description

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.

Who is this book for?

This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.

What you will learn

Learn how to create binary and multi-class deep neural network models

Implement GANs for generating new images

Create autoencoder neural networks for image dimension reduction, image de-noising and image correction

Implement deep neural networks for performing efficient text classification

Learn to define a recurrent convolutional network model for classification in Keras

Explore best practices and tips for performance optimization of various deep learning models

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Vikram Sreedhar Jan 04, 2020

Recommended for all who want to learn NN CNN, GAN,ANN and deep learning in R. Extremely lucid and articulate in explanation

Amazon Verified review

Badshah Mukherjee Apr 06, 2020

The book provides use cases with important concepts which makes it easier for user to understand DL applications. It makes deep learning interesting to start with instead of just focussing on mathematical jargons.Also once the reader gets to know the applications he can refer other books for deeper understanding into the mathematics of DL. This is the perfect book to start DL.

Silvia Jul 25, 2020

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Advanced Deep Learning with R: Become an expert at designing, building, and improving advanced neural network models using R

What do you get with Print?

Advanced Deep Learning with R

Revisiting Deep Learning Architecture and Techniques

Deep learning with R

Deep learning trend

Process of developing a deep network model

Preparing the data for a deep network model

Deep learning techniques with R and RStudio

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