In this section, we will use test data to evaluate the model performance. We can certainly calculate loss and accuracy values using the training data; however, the real test of a classification model is when it is used with unseen data. And since test data is kept separate from the model building process, we can now use it for model evaluation. We will first calculate loss and accuracy values with the test data and then develop a confusion matrix.
Advanced Deep Learning with R
By :
Advanced Deep Learning with R
By:
Overview of this book
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.
Table of Contents (20 chapters)
Preface
Revisiting Deep Learning Architecture and Techniques
Section 2: Deep Learning for Prediction and Classification
Deep Neural Networks for Multi-Class Classification
Deep Neural Networks for Regression
Section 3: Deep Learning for Computer Vision
Image Classification and Recognition
Image Classification Using Convolutional Neural Networks
Applying Autoencoder Neural Networks Using Keras
Image Classification for Small Data Using Transfer Learning
Creating New Images Using Generative Adversarial Networks
Section 4: Deep Learning for Natural Language Processing
Deep Networks for Text Classification
Text Classification Using Recurrent Neural Networks
Text classification Using Long Short-Term Memory Network
Text Classification Using Convolutional Recurrent Neural Networks
Section 5: The Road Ahead
Tips, Tricks, and the Road Ahead
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