After fitting the model, we will evaluate its performance in terms of loss and accuracy. We will also create a confusion matrix to assess classification performance across all 10 types of fashion items. We will perform a model evaluation and prediction for both train and test data. We will also obtain images of fashion items that do not belong to the MNIST fashion data and explore how well the performance of the model can be generalized to new images.
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Revisiting Deep Learning Basics
- Revisiting Deep Learning Architecture and Techniques
- Deep learning with R
- Process of developing a deep network model
- Deep learning techniques with R and RStudio
- Multi-class classification
- Regression problems
- Image classification
- Convolutional neural networks
- Autoencoders
- Transfer learning
- Generative adversarial networks
- Deep network for text classification 
- Recurrent neural networks 
- Long short-term memory network
- Convolutional recurrent networks
- Tips, tricks, and best practices
- Summary
- 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
- Other Books You May Enjoy
Model evaluation and prediction
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