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R Deep Learning Essentials
Deeplearningisapowerfulsubsetofmachinelearningthatisverysuccessfulindomainssuchascomputervisionandnaturallanguageprocessing(NLP).ThissecondeditionofRDeepLearningEssentialswillopenthegatesforyoutoentertheworldofneuralnetworksbybuildingpowerfuldeeplearningmodelsusingtheRecosystem.Thisbookwillintroduceyoutothebasicprinciplesofdeeplearningandteachyoutobuildaneuralnetworkmodelfromscratch.Asyoumakeyourwaythroughthebook,youwillexploredeeplearninglibraries,suchasKeras,MXNet,andTensorFlow,andcreateinterestingdeeplearningmodelsforavarietyoftasksandproblems,includingstructureddata,computervision,textdata,anomalydetection,andrecommendationsystems.You’llcoveradvancedtopics,suchasgenerativeadversarialnetworks(GANs),transferlearning,andlarge-scaledeeplearninginthecloud.Intheconcludingchapters,youwilllearnaboutthetheoreticalconceptsofdeeplearningprojects,suchasmodeloptimization,overfitting,anddataaugmentation,togetherwithotheradvancedtopics.Bytheendofthisbook,youwillbefullypreparedandabletoimplementdeeplearningconceptsinyourresearchworkorprojects.
目錄(177章)
倒序
- 封面
- Title Page
- Copyright and Credits
- R Deep Learning Essentials Second Edition
- 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
- Download the color images
- Conventions used
- Get in touch
- Reviews
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Leave a review - let other readers know what you think 更新時間：2021-08-13 15:34:58
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