Advanced Deep Learning with R: Become an expert at designing, building, and improving advanced neural network models using R

Section 2: Deep Learning for Prediction and Classification

3. Section 2: Deep Learning for Prediction and Classification

4. Deep Neural Networks for Multi-Class Classification

Deep Neural Networks for Multi-Class Classification

Cardiotocogram dataset

Preparing the data for model building

Creating and fitting a deep neural network model

Model evaluation and predictions

Deep Neural Networks for Regression

5. Deep Neural Networks for Regression

Understanding the Boston Housing dataset

Preparing the data

Creating and fitting a deep neural network model for regression

Section 3: Deep Learning for Computer Vision

6. Section 3: Deep Learning for Computer Vision

7. Image Classification and Recognition

Image Classification and Recognition

Handling image data

Data preparation

Creating and fitting the model

Image Classification Using Convolutional Neural Networks

8. Image Classification Using Convolutional Neural Networks

Data preparation

Layers in the convolutional neural networks

Fitting the model

Applying Autoencoder Neural Networks Using Keras

9. Applying Autoencoder Neural Networks Using Keras

Types of autoencoders

Dimension reduction autoencoders

Denoising autoencoders

Image correction

Image Classification for Small Data Using Transfer Learning

10. Image Classification for Small Data Using Transfer Learning

Using a pretrained model to identify an image

Working with the CIFAR10 dataset

Image classification with CNN

Classifying images using the pretrained RESNET50 model

Creating New Images Using Generative Adversarial Networks

11. Creating New Images Using Generative Adversarial Networks

Generative adversarial network overview

Processing MNIST image data

Developing the generator network

Developing the discriminator network

Training the network

Reviewing results

Section 4: Deep Learning for Natural Language Processing

12. Section 4: Deep Learning for Natural Language Processing

13. Deep Networks for Text Classification

Deep Networks for Text Classification

Text datasets

Preparing the data for model building

Developing deep neural networks

Text Classification Using Recurrent Neural Networks

14. Text Classification Using Recurrent Neural Networks

Preparing data for model building

Developing a recurrent neural network model

Fitting the model

Text classification Using Long Short-Term Memory Network

15. Text classification Using Long Short-Term Memory Network

Why do we use LSTM networks?

Preparing text data for model building

Creating a long short-term memory network model

Fitting the LSTM model

Evaluating model performance

Text Classification Using Convolutional Recurrent Neural Networks

16. Text Classification Using Convolutional Recurrent Neural Networks

Working with the reuter_50_50 dataset

Preparing the data for model building

Developing the model architecture

Compiling and fitting the model

Evaluating the model and predicting classes

Section 5: The Road Ahead

17. Section 5: The Road Ahead

18. Tips, Tricks, and the Road Ahead

Tips, Tricks, and the Road Ahead

TensorBoard for training performance visualization

Visualizing deep network models with LIME

Visualizing model training with tfruns

Early stopping of network training