MARC 닫기

• LDR

  05064cam a2200469M 4500

• 001

  000001334760

• 005

  20210114163511

• 006

  m d

• 007

  cr |||||||||||

• 008

  200430s2020 xx o ||| 0 eng d

• 019

  ▼a  1153089714

• 020

  ▼a  1788993780
  ▼q  (electronic bk.)

• 020

  ▼a  9781788993784
  ▼q  (electronic bk.)

• 035

  ▼a  2457359
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1153040549
  ▼z  (OCoLC)1153089714

• 040

  ▼a  YDX
  ▼b  eng
  ▼c  YDX
  ▼d  EBLCP
  ▼d  N$T
  ▼d  OCLCF
  ▼d  248023

• 050

  ▼a  Q325.5

• 082

  ▼a  006.3/1
  ▼2  23

• 100

  ▼a  MICHAEL PAWLUS; RODGER DEVINE.

• 245

  ▼a  HANDS-ON DEEP LEARNING WITH R;A PRACTICAL GUIDE TO DESIGNING, BUILDING, AND IMPROVING NEURAL NETWORK MODELS USING R
  ▼h  [electronic resource].

• 260

  ▼a  [S.l.]:
  ▼b  PACKT PUBLISHING,
  ▼c  2020.

• 300

  ▼a  1 online resource.

• 505

  ▼a  Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Deep Learning Basics -- Chapter 1: Machine Learning Basics -- An overview of machine learning -- Preparing data for modeling -- Handling missing values -- Training a model on prepared data -- Train and test data -- Choosing an algorithm -- Evaluating model results -- Machine learning metrics -- Improving model results -- Reviewing different algorithms -- Summary -- Chapter 2: Setting Up R for Deep Learning -- Technical requirements -- Installing the packages

• 505

  ▼a  Installing ReinforcementLearning -- Installing RBM -- Installing Keras -- Installing H2O -- Installing MXNet -- Preparing a sample dataset -- Exploring Keras -- Available functions -- A Keras example -- Exploring MXNet -- Available functions -- Getting started with MXNet -- Exploring H2O -- Available functions -- An H2O example -- Exploring ReinforcementLearning and RBM -- Reinforcement learning example -- An RBM example -- Comparing the deep learning libraries -- Summary -- Chapter 3: Artificial Neural Networks -- Technical requirements -- Contrasting deep learning with machine learning

• 505

  ▼a  Comparing neural networks and the human brain -- Utilizing bias and activation functions within hidden layers -- Surveying activation functions -- Exploring the sigmoid function -- Investigating the hyperbolic tangent function -- Plotting the rectified linear units activation function -- Calculating the Leaky ReLU activation function -- Defining the swish activation function -- Predicting class likelihood with softmax -- Creating a feedforward network -- Writing a neural network with Base R -- Creating a model with Wisconsin cancer data -- Augmenting our neural network with backpropagation

• 505

  ▼a  Deciding on the hidden layers and neurons -- Training and evaluating the model -- Summary -- Chapter 6: Neural Collaborative Filtering Using Embeddings -- Technical requirements -- Introducing recommender systems -- Collaborative filtering with neural networks -- Exploring embeddings -- Preparing, preprocessing, and exploring data -- Performing exploratory data analysis -- Creating user and item embeddings -- Building and training a neural recommender system -- Evaluating results and tuning hyperparameters -- Hyperparameter tuning -- Adding dropout layers -- Adjusting for user-item bias

• 520

  ▼a  Section 2: Deep Learning Applications -- Chapter 4: CNNs for Image Recognition -- Technical requirements -- Image recognition with shallow nets -- Image recognition with convolutional neural networks -- Optimizers -- Loss functions -- Evaluation metrics -- Enhancing the model with additional layers -- Choosing the most appropriate activation function -- Selecting optimal epochs using dropout and early stopping -- Summary -- Chapter 5: Multilayer Perceptron for Signal Detection -- Technical requirements -- Understanding multilayer perceptrons -- Preparing and preprocessing data

• 520

  ▼a  Deep learning enables efficient and accurate learning from data. Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time.

• 590

  ▼a  Master record variable field(s) change: 050, 082, 650 - OCLC control number change

• 650

  ▼a  Machine learning.

• 650

  ▼a  R (Computer program language)

• 650

  ▼a  Machine learning
  ▼2  fast
  ▼0  (OCoLC)fst01004795

• 650

  ▼a  R (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01086207

• 655

  ▼a  Electronic books.

• 776

  ▼i  Print version:
  ▼a  Pawlus, Michael
  ▼t  Hands-On Deep Learning with R : A Practical Guide to Designing, Building, and Improving Neural Network Models Using R.
  ▼d  Birmingham : Packt Publishing, Limited,c2020

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2457359

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  301250259

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6185679

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2457359

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T