R deep learning essentials a step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet
Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham ; Mumbai :
Packt
[2018]
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| Edición: | Second edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630748706719 |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Table of Contents
- Preface
- Chapter 1: Getting Started with Deep Learning
- What is deep learning?
- A conceptual overview of neural networks
- Neural networks as an extension of linear regression
- Neural networks as a network of memory cells
- Deep neural networks
- Some common myths about deep learning
- Setting up your R environment
- Deep learning frameworks for R
- MXNet
- Keras
- Do I need a GPU (and what is it, anyway)?
- Setting up reproducible results
- Summary
- Chapter 2: Training a Prediction Model
- Neural networks in R
- Building neural network models
- Generating predictions from a neural network
- The problem of overfitting data - the consequences explained
- Use case - building and applying a neural network
- Summary
- Chapter 3: Deep Learning Fundamentals
- Building neural networks from scratch in R
- Neural network web application
- Neural network code
- Back to deep learning
- The symbol, X, y, and ctx parameters
- The num.round and begin.round parameters
- The optimizer parameter
- The initializer parameter
- The eval.metric and eval.data parameters
- The epoch.end.callback parameter
- The array.batch.size parameter
- Using regularization to overcome overfitting
- L1 penalty
- L1 penalty in action
- L2 penalty
- L2 penalty in action
- Weight decay (L2 penalty in neural networks)
- Ensembles and model-averaging
- Use case - improving out-of-sample model performance using dropout
- Summary
- Chapter 4: Training Deep Prediction Models
- Getting started with deep feedforward neural networks
- Activation functions
- Introduction to the MXNet deep learning library
- Deep learning layers
- Building a deep learning model
- Use case - using MXNet for classification and regression.
- Data download and exploration
- Preparing the data for our models
- The binary classification model
- The regression model
- Improving the binary classification model
- The unreasonable effectiveness of data
- Summary
- Chapter 5: Image Classification Using Convolutional Neural Networks
- CNNs
- Convolutional layers
- Pooling layers
- Dropout
- Flatten layers, dense layers, and softmax
- Image classification using the MXNet library
- Base model (no convolutional layers)
- LeNet
- Classification using the fashion MNIST dataset
- References/further reading
- Summary
- Chapter 6: Tuning and Optimizing Models
- Evaluation metrics and evaluating performance
- Types of evaluation metric
- Evaluating performance
- Data preparation
- Different data distributions
- Data partition between training, test, and validation sets
- Standardization
- Data leakage
- Data augmentation
- Using data augmentation to increase the training data
- Test time augmentation
- Using data augmentation in deep learning libraries
- Tuning hyperparameters
- Grid search
- Random search
- Use case-using LIME for interpretability
- Model interpretability with LIME
- Summary
- Chapter 7: Natural Language Processing Using Deep Learning
- Document classification
- The Reuters dataset
- Traditional text classification
- Deep learning text classification
- Word vectors
- Comparing traditional text classification and deep learning
- Advanced deep learning text classification
- 1D convolutional neural network model
- Recurrent neural network model
- Long short term memory model
- Gated Recurrent Units model
- Bidirectional LSTM model
- Stacked bidirectional model
- Bidirectional with 1D convolutional neural network model
- Comparing the deep learning NLP architectures
- Summary
- Chapter 8: Deep Learning Models Using TensorFlow in R.
- Introduction to the TensorFlow library
- Using TensorBoard to visualize deep learning networks
- TensorFlow models
- Linear regression using TensorFlow
- Convolutional neural networks using TensorFlow
- TensorFlow estimators and TensorFlow runs packages
- TensorFlow estimators
- TensorFlow runs package
- Summary
- Chapter 9: Anomaly Detection and Recommendation Systems
- What is unsupervised learning?
- How do auto-encoders work?
- Regularized auto-encoders
- Penalized auto-encoders
- Denoising auto-encoders
- Training an auto-encoder in R
- Accessing the features of the auto-encoder model
- Using auto-encoders for anomaly detection
- Use case - collaborative filtering
- Preparing the data
- Building a collaborative filtering model
- Building a deep learning collaborative filtering model
- Applying the deep learning model to a business problem
- Summary
- Chapter 10: Running Deep Learning Models in the Cloud
- Setting up a local computer for deep learning
- How do I know if my model is training on a GPU?
- Using AWS for deep learning
- A brief introduction to AWS
- Creating a deep learning GPU instance in AWS
- Creating a deep learning AMI in AWS
- Using Azure for deep learning
- Using Google Cloud for deep learning
- Using Paperspace for deep learning
- Summary
- Chapter 11: The Next Level in Deep Learning
- Image classification models
- Building a complete image classification solution
- Creating the image data
- Building the deep learning model
- Using the saved deep learning model
- The ImageNet dataset
- Loading an existing model
- Transfer learning
- Deploying TensorFlow models
- Other deep learning topics
- Generative adversarial networks
- Reinforcement learning
- Additional deep learning resources
- Summary
- Other Books You May Enjoy
- Index.
