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Deep Learning with R for Beginners
Deeplearningfindspracticalapplicationsinseveraldomains,whileRisthepreferredlanguagefordesigninganddeployingdeeplearningmodels.ThisLearningPathintroducesyoutothebasicsofdeeplearningandeventeachesyoutobuildaneuralnetworkmodelfromscratch.Asyoumakeyourwaythroughthechapters,you’llexploredeeplearninglibrariesandunderstandhowtocreatedeeplearningmodelsforavarietyofchallenges,rightfromanomalydetectiontorecommendationsystems.Thebookwillthenhelpyoucoveradvancedtopics,suchasgenerativeadversarialnetworks(GANs),transferlearning,andlarge-scaledeeplearninginthecloud,inadditiontomodeloptimization,overfitting,anddataaugmentation.Throughreal-worldprojects,you’llalsogetuptospeedwithtrainingconvolutionalneuralnetworks(CNNs),recurrentneuralnetworks(RNNs),andlongshort-termmemorynetworks(LSTMs)inR.BytheendofthisLearningPath,you’llbewellversedwithdeeplearningandhavetheskillsyouneedtoimplementanumberofdeeplearningconceptsinyourresearchworkorprojects.ThisLearningPathincludescontentfromthefollowingPacktproducts:RDeepLearningEssentials-SecondEditionbyJoshuaF.WileyandMarkHodnett.RDeepLearningProjectsbyYuxi(Hayden)LiuandPabloMaldonado.
目錄(265章)
倒序
- coverpage
- Title Page
- Copyright and Credits
- Deep Learning with R for Beginners
- About Packt
- Why subscribe?
- Packt.com
- Contributors
- About the authors
- 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 of this book
- 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
- Summary
- 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 Signs 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 Embedding
- 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 更新時間：2021-06-24 14:31:23
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