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Preface
Chapter. 1: Handwritten Digit Recognition using Convolutional Nerual 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 representiotn with CNNs

Chapter.2: 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 stared with Exploring GTSRB
First solution-convolutional neural networks suing MXNet
Trying something new-CNNs using Keras with TensorFlow
Reducing overfitiing with dropout
Dealing with a small training set-data augmentation
Reviewing methods to prevent overfitting in CNNs
Summary

Chapter. 3: Fraud Detection with Atoencoders
Getting ready
Installing Keras and TensorFlow for R
Instaling H2O
Our first examples
A simple 2D Example
Autoencoder and MNIST
Outlier detection in MNIST
Credit card fraud detection with autoencoders
Exploratory data analysis
The autoencoder approach-Keras
Fraud detection with H2O
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

Chaper. 4: 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

Chapter. 5: Sntiment Analysis with Word Embeddings
Warm-up-date 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
Sentimnet analysis from movie reviews
Data preprocessing
From words to vectors
Sentiment extraction
The importance of data cleansing
Vector embeddings and nerual 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

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Index