Introduction to Deep Learning Using R

Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn

• Understand the intuition and mathematics that power deep learning models

• Utilize various algorithms using the R programming language and its packages

• Use best practices for experimental design and variable selection

• Practice the methodology to approach and effectively solve problems as a data scientist

• Evaluate the effectiveness of algorithmic solutions and enhance their predictive power

  Who This Book Is For
  Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.

  The code in this book utilizes R studio and its packages, all of which are open source, to make the learning process as simple as possible Each chapter builds upon the knowledge of the preceding chapter The book has two main sections: Theory and Applications. Although theory will be covered in depth, the purpose of this book is to give readers the necessary knowledge to apply these models.

  Autorentext
  Taweh Beysolow II is a Machine Learning Scientist currently based in the United States with a passion for research and applying machine learning methods to solve problems. He has a Bachelor of Science degree in Economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. Currently, he is extremely passionate about all matters related to machine learning, data science, quantitative finance, and economics.

  Inhalt
  Chapter 1: What is Deep Learning?.- Chapter 2: Mathematical Review.- Chapter 3: A Review of Optimization and Machine Learning.- Chapter 4: Single and Multi-Layer Perceptron Models.- Chapter 5: Convolutional Neural Networks (CNNs).- Chapter 6: Recurrent Neural Networks (RNNs) .- Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks.- Chapter 8: Experimental Design and Heuristics .- Chapter 9: Deep Learning and Machine Learning Hardware/Software Suggestions.- Chapter 10: Machine Learning Example Problems .- Chapter 11: Deep Learning and Other Example Problems .- Chapter 12: Closing Statements.-