| LDR | | 05064cmm u2200469M 4500 |
| 001 | | 000000317750 |
| 003 | | OCoLC |
| 005 | | 20230525183313 |
| 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 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a Q325.5 |
| 082 | 04 |
▼a 006.3/1
▼2 23 |
| 100 | 1 |
▼a MICHAEL PAWLUS; RODGER DEVINE. |
| 245 | 10 |
▼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 | 0 |
▼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 | 8 |
▼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 | 8 |
▼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 | 8 |
▼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 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a R (Computer program language) |
| 650 | 7 |
▼a Machine learning
▼2 fast
▼0 (OCoLC)fst01004795 |
| 650 | 7 |
▼a R (Computer program language)
▼2 fast
▼0 (OCoLC)fst01086207 |
| 655 | 4 |
▼a Electronic books. |
| 776 | 08 |
▼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 | 40 |
▼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 관리자 |
| 994 | |
▼a 92
▼b N$T |