R Deep Learning Projects
5 real-world projects to help you master deep learning concepts About This Book Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing
2018.
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| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630598106719 |
Tabla de Contenidos:
- Cover
- Copyright and Credits
- Packt Upsell
- Contributors
- Table of Contents
- Preface
- Chapter 1: 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
- 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 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
- Chapter 3: 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
- Chapter 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: 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
- Index.
