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Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs.
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Mi 4500 | ||
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| 019 | |a 1175641884 | ||
| 020 | |a 9781788996525 | ||
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| 020 | |a 178899745X |q (Trade Paper) | ||
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| 082 | 0 | 4 | |a 005.133 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Karim, Rezaul. | |
| 245 | 1 | 0 | |a Java Deep Learning Projects : |b Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (428 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 500 | |a Sentiment analysis using Word2Vec and LSTM. | ||
| 520 | |a 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. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Java. | |
| 650 | 0 | |a Application program interfaces. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Application software |x Development. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Logiciels d'application |x Développement. | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Application software |x Development |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 758 | |i has work: |a JAVA DEEP LEARNING PROJECTS;IMPLEMENT 10 REALRLD DEEP LEARNING APPLICATIONS USING DEEPLEARNING4J AND OPEN SOURCE APIS (Text) |1 https://id.oclc.org/worldcat/entity/E39PCXTtXFrpDV69D3hrpx3Q83 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Karim, Rezaul. |t Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781788997454 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=5446048 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH34796353 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5446048 | ||
| 994 | |a 92 |b IZTAP | ||