Taweh Beysolow Ii

Introduction to Deep Learning Using R (eBook, PDF)

A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R

• Format: PDF

Produktbeschreibung

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 Will 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

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Produktdetails

• Verlag: Apress
• Seitenzahl: 227
• Erscheinungstermin: 19. Juli 2017
• Englisch
• ISBN-13: 9781484227343
• Artikelnr.: 53059526

Autorenporträt

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.

Inhaltsangabe

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.-