Now, we will evaluate the performance of this model for the training and test data. Calculations relating to the loss, accuracy, and confusion matrix will be carried out so that we can evaluate the model image's classification performance. We will also obtain the accuracy for each of the 10 categories.
- 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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