Hands-on Deep Learning with R

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  • Build a feedforward neural network to see how the activation function computes an output
  • Create an image recognition model using convolutional neural networks(CNNs)
  • Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm
  • Apply text cleaning techniques to remove uninformative text using natural language processing (NLP)
  • Build, train, and evaluate a GAN model for face generation
  • Understand the concept and implementation of reinforcement learning in R

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you solve a number of challenges using various deep learning algorithms and architectures with R.

The book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You will understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters and evaluate model performance. You will even cover various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling and forecast stock market prices. Toward the end of the book, you will learn the common applications of generative adversarial networks (GANs) and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems.

By the end of this book, you will be able to build and deploy your own deep learning applications using appropriate deep learning frameworks and algorithms.

  • Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem
  • Improve models using parameter tuning, feature engineering, and ensembling
  • Apply advanced neural network models such as deep autoencoders and GANs across different domains
Page Count 229
Course Length 6 hours 52 minutes
ISBN 9781788996839
Date Of Publication 15 May 2020


Michael Pawlus

Michael Pawlus is a Data Scientist at the University of Southern California his expertise are in R Programming, deep learning, machine learning. He has a Master’s degree from the University of Sheffield. He has 7 years of experience in using machine learning and deep learning algorithms within the R environment to solve data science challenges in higher education.

Rodger Devine

Rodger Devine is a business intelligence professional with executive leadership, strategy, research, analytics, change management, and diverse operational experience. He has a proven ability to lead and manage cross-functional teams to meet complex project goals and deadlines within budget. He also has prepared graduate coursework in data analysis, predictive modeling, information retrieval, machine learning, natural language processing, IT organizational management, digital security, and network analysis.