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BL
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| Record Number
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858116
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| Main Entry
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Karim, Rezaul.
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| Title & Author
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Java Deep Learning Projects : : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.
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| Publication Statement
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Birmingham :: Packt Publishing Ltd,, 2018.
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| Page. NO
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1 online resource (428 pages)
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| ISBN
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1788996526
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: 178899745X
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: 9781788996525
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: 9781788997454
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| Notes
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Sentiment analysis using Word2Vec and LSTM.
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| Contents
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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.
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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.
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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.
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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.
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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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| Abstract
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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.
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| Subject
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Application program interfaces.
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Application software-- Development.
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Java.
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Machine learning.
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Application software-- Development.
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Artificial intelligence.
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Computers-- Intelligence (AI) Semantics.
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Computers-- Natural Language Processing.
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Computers-- Neural Networks.
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Machine learning.
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Natural language machine translation.
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| Subject
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Neural networks fuzzy systems.
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| Dewey Classification
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005.133
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| LC Classification
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QA76.73.J38.K375 2018
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