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Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.

Author: Rezaul Karim
Publisher: Birmingham : Packt Publishing Ltd, 2018.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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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Genre/Form: Electronic books
Additional Physical Format: Print version:
Karim, Rezaul.
Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.
Birmingham : Packt Publishing Ltd, ©2018
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Rezaul Karim
ISBN: 9781788996525 1788996526
OCLC Number: 1044949296
Notes: Sentiment analysis using Word2Vec and LSTM.
Description: 1 online resource (428 pages)
Contents: 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. 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. 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. 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. 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.

Abstract:

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  Read more...

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