R Deep Learning Projects

Table of contents

1. Title Page
2. Copyright and Credits
   1. R Deep Learning Projects
3. Packt Upsell
   1. Why subscribe?
   2. PacktPub.com
4. Contributors
   1. About the authors
   2. About the reviewer
   3. Packt is searching for authors like you
5. Preface
   1. Who this book is for
   2. What this book covers
   3. To get the most out of this book
      1. Download the example code files
      2. Conventions used
   4. Get in touch
      1. Reviews
6. Handwritten Digit Recognition Using Convolutional Neural Networks
   1. What is deep learning and why do we need it?
      1. What makes deep learning special?
      2. What are the applications of deep learning?
   2. Handwritten digit recognition using CNNs
      1. Get started with exploring MNIST
      2. First attempt – logistic regression
      3. Going from logistic regression to single-layer neural networks
      4. Adding more hidden layers to the networks
      5. Extracting richer representation with CNNs
   3. Summary
7. Traffic Sign Recognition for Intelligent Vehicles
   1. How is deep learning applied in self-driving cars?
      1. How does deep learning become a state-of-the-art solution?
   2. Traffic sign recognition using CNN
      1. Getting started with exploring GTSRB
      2. First solution – convolutional neural networks using MXNet
      3. Trying something new – CNNs using Keras with TensorFlow
      4. Reducing overfitting with dropout
   3. Dealing with a small training set – data augmentation
   4. Reviewing methods to prevent overfitting in CNNs
   5. Summary
8. Fraud Detection with Autoencoders
   1. Getting ready
      1. Installing Keras and TensorFlow for R
      2. Installing H2O
   2. Our first examples
      1. A simple 2D example
      2. Autoencoders and MNIST
      3. Outlier detection in MNIST
   3. Credit card fraud detection with autoencoders
      1. Exploratory data analysis
      2. The autoencoder approach – Keras
      3. Fraud detection with H2O
      4. Exercises
   4. Variational Autoencoders
      1. Image reconstruction using VAEs
      2. Outlier detection in MNIST
   5. Text fraud detection
      1. From unstructured text data to a matrix
      2. From text to matrix representation — the Enron dataset
      3. Autoencoder on the matrix representation
      4. Exercises
   6. Summary
9. Text Generation Using Recurrent Neural Networks
   1. What is so exciting about recurrent neural networks?
      1. But what is a recurrent neural network, really?
      2. LSTM and GRU networks
         1. LSTM
         2. GRU
   2. RNNs from scratch in R
      1. Classes in R with R6
         1. Perceptron as an R6 class
         2. Logistic regression
         3. Multi-layer perceptron
      2. Implementing a RNN
         1. Implementation as an R6 class
         2. Implementation without R6
         3. RNN without derivatives — the cross-entropy method
   3. RNN using Keras
      1. A simple benchmark implementation
      2. Generating new text from old
      3. Exercises
   4. Summary
10. Sentiment Analysis with Word Embeddings
    1. Warm-up – data exploration
       1. Working with tidy text
       2. The more, the merrier – calculating n-grams instead of single words
    2. Bag of words benchmark
       1. Preparing the data
       2. Implementing a benchmark – logistic regression 
       3. Exercises
    3. Word embeddings
       1. word2vec
       2. GloVe
    4. Sentiment analysis from movie reviews
       1. Data preprocessing
       2. From words to vectors
       3. Sentiment extraction
       4. The importance of data cleansing
       5. Vector embeddings and neural networks
       6. Bi-directional LSTM networks
       7. Other LSTM architectures
       8. Exercises
    5. Mining sentiment from Twitter
       1. Connecting to the Twitter API
       2. Building our model
          1. Exploratory data analysis
          2. Using a trained model
    6. Summary
11. Other Books You May Enjoy
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