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