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R Deep Learning Cookbook
最新章節：
See also
Datascienceprofessionalsoranalystswhohaveperformedmachinelearningtasksandnowwanttoexploredeeplearningandwantaquickreferencethatcouldaddressthepainpointswhileimplementingdeeplearning.Thosewhowishtohaveanedgeoverotherdeeplearningprofessionalswillfindthisbookquiteuseful.
目錄(312章)
倒序
- cover
- Title Page
- Copyright
- R Deep Learning Cookbook
- Credits
- About the Authors
- About the Reviewer
- www.PacktPub.com
- Why subscribe?
- Customer Feedback
- Preface
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Downloading the color images of this book
- Errata
- Piracy
- Questions
- Getting Started
- Introduction
- Installing R with an IDE
- Getting ready
- How to do it...
- Installing a Jupyter Notebook application
- How to do it...
- There's more...
- Starting with the basics of machine learning in R
- How to do it...
- How it works...
- Setting up deep learning tools/packages in R
- How to do it...
- Installing MXNet in R
- Getting ready
- How to do it...
- Installing TensorFlow in R
- Getting ready
- How to do it...
- How it works...
- See also
- Installing H2O in R
- Getting ready
- How to do it...
- How it works...
- There's more...
- Installing all three packages at once using Docker
- Getting ready
- How to do it...
- There's more...
- Deep Learning with R
- Starting with logistic regression
- Getting ready
- How to do it...
- Introducing the dataset
- Getting ready
- How to do it...
- Performing logistic regression using H2O
- Getting ready
- How to do it...
- How it works...
- See also
- Performing logistic regression using TensorFlow
- Getting ready
- How to do it...
- How it works...
- Visualizing TensorFlow graphs
- Getting ready
- How to do it...
- How it works...
- Starting with multilayer perceptrons
- Getting ready
- How to do it...
- There's more...
- See also
- Setting up a neural network using H2O
- Getting ready
- How to do it...
- How it works...
- Tuning hyper-parameters using grid searches in H2O
- Getting ready
- How to do it...
- How it works...
- Setting up a neural network using MXNet
- Getting ready
- How to do it...
- How it works...
- Setting up a neural network using TensorFlow
- Getting ready
- How to do it...
- How it works...
- There's more...
- Convolution Neural Network
- Introduction
- Downloading and configuring an image dataset
- Getting ready
- How to do it...
- How it works...
- See also
- Learning the architecture of a CNN classifier
- Getting ready
- How to do it...
- How it works...
- Using functions to initialize weights and biases
- Getting ready
- How to do it...
- How it works...
- Using functions to create a new convolution layer
- Getting ready
- How to do it...
- How it works...
- Using functions to create a new convolution layer
- Getting ready
- How to do it...
- How it works...
- Using functions to flatten the densely connected layer
- Getting ready
- How to do it...
- How it works...
- Defining placeholder variables
- Getting ready
- How to do it...
- How it works...
- Creating the first convolution layer
- Getting ready
- How to do it...
- How it works...
- Creating the second convolution layer
- Getting ready
- How to do it...
- How it works...
- Flattening the second convolution layer
- Getting ready
- How to do it...
- How it works...
- Creating the first fully connected layer
- Getting ready
- How to do it...
- How it works...
- Applying dropout to the first fully connected layer
- Getting ready
- How to do it...
- How it works...
- Creating the second fully connected layer with dropout
- Getting ready
- How to do it...
- How it works...
- Applying softmax activation to obtain a predicted class
- Getting ready
- How to do it...
- Defining the cost function used for optimization
- Getting ready
- How to do it...
- How it works...
- Performing gradient descent cost optimization
- Getting ready
- How to do it...
- Executing the graph in a TensorFlow session
- Getting ready
- How to do it...
- How it works...
- Evaluating the performance on test data
- Getting ready
- How to do it...
- How it works...
- Data Representation Using Autoencoders
- Introduction
- Setting up autoencoders
- Getting ready
- How to do it...
- Data normalization
- Getting ready
- Visualizing dataset distribution
- How to do it...
- How to set up an autoencoder model
- Running optimization
- Setting up a regularized autoencoder
- Getting ready
- How to do it...
- How it works...
- Fine-tuning the parameters of the autoencoder
- Setting up stacked autoencoders
- Getting ready
- How to do it...
- Setting up denoising autoencoders
- Getting ready
- How to do it...
- Reading the dataset
- Corrupting data to train
- Setting up a denoising autoencoder
- How it works...
- Building and comparing stochastic encoders and decoders
- Getting ready
- How to do it...
- Setting up a VAE model
- Output from the VAE autoencoder
- Learning manifolds from autoencoders
- How to do it...
- Setting up principal component analysis
- Evaluating the sparse decomposition
- Getting ready
- How to do it...
- How it works...
- Generative Models in Deep Learning
- Comparing principal component analysis with the Restricted Boltzmann machine
- Getting ready
- How to do it...
- Setting up a Restricted Boltzmann machine for Bernoulli distribution input
- Getting ready
- How to do it...
- Training a Restricted Boltzmann machine
- Getting ready
- Example of a sampling
- How to do it...
- Backward or reconstruction phase of RBM
- Getting ready
- How to do it...
- Understanding the contrastive divergence of the reconstruction
- Getting ready
- How to do it...
- How it works...
- Initializing and starting a new TensorFlow session
- Getting ready
- How to do it...
- How it works...
- Evaluating the output from an RBM
- Getting ready
- How to do it...
- How it works...
- Setting up a Restricted Boltzmann machine for Collaborative Filtering
- Getting ready
- How to do it...
- Performing a full run of training an RBM
- Getting ready
- How to do it...
- Setting up a Deep Belief Network
- Getting ready
- How to do it...
- How it works...
- Implementing a feed-forward backpropagation Neural Network
- Getting ready
- How to do it...
- How it works...
- Setting up a Deep Restricted Boltzmann Machine
- Getting ready
- How to do it...
- How it works...
- Recurrent Neural Networks
- Setting up a basic Recurrent Neural Network
- Getting ready
- How to do it...
- How it works...
- Setting up a bidirectional RNN model
- Getting ready
- How to do it...
- Setting up a deep RNN model
- How to do it...
- Setting up a Long short-term memory based sequence model
- How to do it...
- How it works...
- Reinforcement Learning
- Introduction
- Setting up a Markov Decision Process
- Getting ready
- How to do it...
- Performing model-based learning
- How to do it...
- Performing model-free learning
- Getting ready
- How to do it...
- Application of Deep Learning in Text Mining
- Performing preprocessing of textual data and extraction of sentiments
- How to do it...
- How it works...
- Analyzing documents using tf-idf
- How to do it...
- How it works...
- Performing sentiment prediction using LSTM network
- How to do it...
- How it works...
- Application using text2vec examples
- How to do it...
- How it works...
- Application of Deep Learning to Signal processing
- Introducing and preprocessing music MIDI files
- Getting ready
- How to do it...
- Building an RBM model
- Getting ready
- How to do it...
- Generating new music notes
- How to do it...
- Transfer Learning
- Introduction
- Illustrating the use of a pretrained model
- Getting ready
- How to do it...
- Setting up the Transfer Learning model
- Getting ready
- How to do it...
- Building an image classification model
- Getting ready
- How to do it...
- Training a deep learning model on a GPU
- Getting ready
- How to do it...
- Comparing performance using CPU and GPU
- Getting ready
- How to do it...
- There's more...
- See also 更新時間：2021-07-02 20:49:59
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