H2O implements best-in-class algorithms at scale, such as distributed random forest, gradient boosting, and deep learning. H2O also includes a Stacked. Machine Learning and Data Science - An Introduction to Statistical Learning Methods with R 2015.epub: First commit: Feb 19, 2017: Machine Learning and Data Science - An Introduction to Statistical Learning Methods with R 2015.pdf: First commit: Feb 19, 2017: Machine Learning for Hackers - Case Studies and Algorithms to Get You Started 2012.pdf.
Deep Learning with R
- Author: Abhijit Ghatak
- Publisher: Springer
- ISBN: 9811358508
- Category: Computers
- Page: 245
- View: 3225
Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks.
This is the code repository for Machine Learning with R - Third Edition, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
About the Book
Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.
Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.
This new 3rd edition updates the classic R data science book with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.
Instructions and Navigation
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02. Pianoteq 6 crack.
The code will look like the following: