عنوان
پدید آورنده
موضوع
رده
QA76.73.J38 .K375 2018
QA76
.
73
.
J38
.
K375
2018
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
1788996526
(Number (ISBN
178899745X
(Number (ISBN
9781788996525
(Number (ISBN
9781788997454
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Java Deep Learning Projects :
General Material Designation
[Book]
Other Title Information
Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Birmingham :
Name of Publisher, Distributor, etc.
Packt Publishing Ltd,
Date of Publication, Distribution, etc.
2018.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (428 pages)
GENERAL NOTES
Text of Note
Sentiment analysis using Word2Vec and LSTM.
CONTENTS NOTE
Text of Note
Intro; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; A soft introduction to ML; Working principles of ML algorithms; Supervised learning; Unsupervised learning; Reinforcement learning; Putting ML tasks altogether; Delving into deep learning; How did DL take ML into next level?; Artificial Neural Networks; Biological neurons; A brief history of ANNs; How does an ANN learn?; ANNs and the backpropagation algorithm; Forward and backward passes; Weights and biases; Weight optimization; Activation functions.
Text of Note
Frequently asked questions (FAQs)Summary; Answers to FAQs; Chapter 2: Cancer Types Prediction Using Recurrent Type Networks; Deep learning in cancer genomics; Cancer genomics dataset description; Preparing programming environment; Titanic survival revisited with DL4J; Multilayer perceptron network construction; Hidden layer 1; Hidden layer 2; Output layer; Network training; Evaluating the model; Cancer type prediction using an LSTM network; Dataset preparation for training; Recurrent and LSTM networks; Dataset preparation; LSTM network construction; Network training; Evaluating the model.
Text of Note
Frequently asked questions (FAQs)Summary; Answers to questions; Chapter 3: Multi-Label Image Classification Using Convolutional Neural Networks; Image classification and drawbacks of DNNs; CNN architecture; Convolutional operations; Pooling and padding operations; Fully connected layer (dense layer); Multi-label image classification using CNNs; Problem description; Description of the dataset; Removing invalid images; Workflow of the overall project; Image preprocessing; Extracting image metadata; Image feature extraction; Preparing the ND4J dataset.
Text of Note
Neural network architecturesDeep neural networks; Multilayer Perceptron; Deep belief networks; Autoencoders; Convolutional neural networks; Recurrent neural networks ; Emergent architectures; Residual neural networks; Generative adversarial networks; Capsule networks; DL frameworks and cloud platforms; Deep learning frameworks; Cloud-based platforms for DL; Deep learning from a disaster -- Titanic survival prediction; Problem description; Configuring the programming environment; Feature engineering and input dataset preparation; Training MLP classifier ; Evaluating the MLP classifier.
Text of Note
Training, evaluating, and saving the trained CNN modelsNetwork construction; Scoring the model; Submission file generation; Wrapping everything up by executing the main() method; Frequently asked questions (FAQs); Summary; Answers to questions; Chapter 4: Sentiment Analysis Using Word2Vec and LSTM Network; Sentiment analysis is a challenging task; Using Word2Vec for neural word embeddings; Datasets and pre-trained model description; Large Movie Review dataset for training and testing; Folder structure of the dataset; Description of the sentiment labeled dataset; Word2Vec pre-trained model.
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8
8
8
8
SUMMARY OR ABSTRACT
Text of Note
You will build full-fledged, deep learning applications with Java and different open-source libraries. Master numerical computing, deep learning, and the latest Java programming features to carry out complex advanced tasks. This book is filled with best practices/tips after every project to help you optimize your deep learning models with ease.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
01201872
Stock Number
B10335
OTHER EDITION IN ANOTHER MEDIUM
Title
Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.
International Standard Book Number
9781788997454
TOPICAL NAME USED AS SUBJECT
Application program interfaces.
Application software-- Development.
Java.
Machine learning.
Application software-- Development.
Artificial intelligence.
Computers-- Intelligence (AI) & Semantics.
Computers-- Natural Language Processing.
Computers-- Neural Networks.
Machine learning.
Natural language & machine translation.
Neural networks & fuzzy systems.
DEWEY DECIMAL CLASSIFICATION
Number
005
.
133
LIBRARY OF CONGRESS CLASSIFICATION
Class number
QA76
.
73
.
J38
Book number
.
K375
2018
PERSONAL NAME - PRIMARY RESPONSIBILITY
Karim, Rezaul.
ORIGINATING SOURCE
Date of Transaction
20200823064244.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
