R Deep Learning Cookbook:
bPowerful, independent recipes to build deep learning models in different application areas using R libraries/bh2About This Book/h2ulliMaster intricacies of R deep learning packages such as mxnet & tensorflow/liliLearn application on deep learning in different domains using practical example...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2017
|
| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bPowerful, independent recipes to build deep learning models in different application areas using R libraries/bh2About This Book/h2ulliMaster intricacies of R deep learning packages such as mxnet & tensorflow/liliLearn application on deep learning in different domains using practical examples from text, image and speech/liliGuide to set-up deep learning models using CPU and GPU/li/ulh2Who This Book Is For/h2Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.h2What You Will Learn/h2ulliBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet./liliAnalyzing a Deep boltzmann machine/liliSetting up and Analysing Deep belief networks/liliBuilding supervised model using various machine learning algorithms/liliSet up variants of basic convolution function/liliRepresent data using Autoencoders./liliExplore generative models available in Deep Learning./liliDiscover sequence modeling using Recurrent nets/liliLearn fundamentals of Reinforcement Leaning/liliLearn the steps involved in applying Deep Learning in text mining/liliExplore application of deep learning in signal processing/liliUtilize Transfer learning for utilizing pre-trained model/liliTrain a deep learning model on a GPU/li/ulh2In Detail/h2Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. |
| Beschreibung: | 1 Online-Ressource (288 Seiten) |
| ISBN: | 9781787127111 |
Internformat
MARC
| LEADER | 00000nmm a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV047070012 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 201218s2017 |||| o||u| ||||||eng d | ||
| 020 | |a 9781787127111 |9 978-1-78712-711-1 | ||
| 035 | |a (ZDB-5-WPSE)9781787127111288 | ||
| 035 | |a (OCoLC)1227476350 | ||
| 035 | |a (DE-599)BVBBV047070012 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Prakash, Dr. PKS |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a R Deep Learning Cookbook |c Prakash, Dr. PKS |
| 250 | |a 1 | ||
| 264 | 1 | |a Birmingham |b Packt Publishing Limited |c 2017 | |
| 300 | |a 1 Online-Ressource (288 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | |a bPowerful, independent recipes to build deep learning models in different application areas using R libraries/bh2About This Book/h2ulliMaster intricacies of R deep learning packages such as mxnet & tensorflow/liliLearn application on deep learning in different domains using practical examples from text, image and speech/liliGuide to set-up deep learning models using CPU and GPU/li/ulh2Who This Book Is For/h2Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. | ||
| 520 | |a Those who wish to have an edge over other deep learning professionals will find this book quite useful.h2What You Will Learn/h2ulliBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet./liliAnalyzing a Deep boltzmann machine/liliSetting up and Analysing Deep belief networks/liliBuilding supervised model using various machine learning algorithms/liliSet up variants of basic convolution function/liliRepresent data using Autoencoders./liliExplore generative models available in Deep Learning./liliDiscover sequence modeling using Recurrent nets/liliLearn fundamentals of Reinforcement Leaning/liliLearn the steps involved in applying Deep Learning in text mining/liliExplore application of deep learning in signal processing/liliUtilize Transfer learning for utilizing pre-trained model/liliTrain a deep learning model on a GPU/li/ulh2In Detail/h2Deep Learning is the next big thing. It is a part of machine learning. | ||
| 520 | |a It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. | ||
| 650 | 4 | |a COMPUTERS / Intelligence (AI) & | |
| 650 | 4 | |a Semantics | |
| 650 | 4 | |a COMPUTERS / Natural Language Processing | |
| 700 | 1 | |a Rao, Achyutuni Sri Krishna |e Sonstige |4 oth | |
| 912 | |a ZDB-5-WPSE | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032477038 | ||
Datensatz im Suchindex
| _version_ | 1804182072430952448 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Prakash, Dr. PKS |
| author_facet | Prakash, Dr. PKS |
| author_role | aut |
| author_sort | Prakash, Dr. PKS |
| author_variant | d p p dp dpp |
| building | Verbundindex |
| bvnumber | BV047070012 |
| collection | ZDB-5-WPSE |
| ctrlnum | (ZDB-5-WPSE)9781787127111288 (OCoLC)1227476350 (DE-599)BVBBV047070012 |
| edition | 1 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03226nmm a2200361zc 4500</leader><controlfield tag="001">BV047070012</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">201218s2017 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781787127111</subfield><subfield code="9">978-1-78712-711-1</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-5-WPSE)9781787127111288</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1227476350</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047070012</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Prakash, Dr. PKS</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">R Deep Learning Cookbook</subfield><subfield code="c">Prakash, Dr. PKS</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham</subfield><subfield code="b">Packt Publishing Limited</subfield><subfield code="c">2017</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (288 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">bPowerful, independent recipes to build deep learning models in different application areas using R libraries/bh2About This Book/h2ulliMaster intricacies of R deep learning packages such as mxnet &amp; tensorflow/liliLearn application on deep learning in different domains using practical examples from text, image and speech/liliGuide to set-up deep learning models using CPU and GPU/li/ulh2Who This Book Is For/h2Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Those who wish to have an edge over other deep learning professionals will find this book quite useful.h2What You Will Learn/h2ulliBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet./liliAnalyzing a Deep boltzmann machine/liliSetting up and Analysing Deep belief networks/liliBuilding supervised model using various machine learning algorithms/liliSet up variants of basic convolution function/liliRepresent data using Autoencoders./liliExplore generative models available in Deep Learning./liliDiscover sequence modeling using Recurrent nets/liliLearn fundamentals of Reinforcement Leaning/liliLearn the steps involved in applying Deep Learning in text mining/liliExplore application of deep learning in signal processing/liliUtilize Transfer learning for utilizing pre-trained model/liliTrain a deep learning model on a GPU/li/ulh2In Detail/h2Deep Learning is the next big thing. It is a part of machine learning. </subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. </subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Intelligence (AI) &amp</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Semantics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">COMPUTERS / Natural Language Processing</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rao, Achyutuni Sri Krishna</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-5-WPSE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032477038</subfield></datafield></record></collection> |
| id | DE-604.BV047070012 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T16:13:34Z |
| indexdate | 2024-07-10T09:01:44Z |
| institution | BVB |
| isbn | 9781787127111 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032477038 |
| oclc_num | 1227476350 |
| open_access_boolean | |
| physical | 1 Online-Ressource (288 Seiten) |
| psigel | ZDB-5-WPSE |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Packt Publishing Limited |
| record_format | marc |
| spelling | Prakash, Dr. PKS Verfasser aut R Deep Learning Cookbook Prakash, Dr. PKS 1 Birmingham Packt Publishing Limited 2017 1 Online-Ressource (288 Seiten) txt rdacontent c rdamedia cr rdacarrier bPowerful, independent recipes to build deep learning models in different application areas using R libraries/bh2About This Book/h2ulliMaster intricacies of R deep learning packages such as mxnet & tensorflow/liliLearn application on deep learning in different domains using practical examples from text, image and speech/liliGuide to set-up deep learning models using CPU and GPU/li/ulh2Who This Book Is For/h2Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.h2What You Will Learn/h2ulliBuild deep learning models in different application areas using TensorFlow, H2O, and MXnet./liliAnalyzing a Deep boltzmann machine/liliSetting up and Analysing Deep belief networks/liliBuilding supervised model using various machine learning algorithms/liliSet up variants of basic convolution function/liliRepresent data using Autoencoders./liliExplore generative models available in Deep Learning./liliDiscover sequence modeling using Recurrent nets/liliLearn fundamentals of Reinforcement Leaning/liliLearn the steps involved in applying Deep Learning in text mining/liliExplore application of deep learning in signal processing/liliUtilize Transfer learning for utilizing pre-trained model/liliTrain a deep learning model on a GPU/li/ulh2In Detail/h2Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Natural Language Processing Rao, Achyutuni Sri Krishna Sonstige oth |
| spellingShingle | Prakash, Dr. PKS R Deep Learning Cookbook COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Natural Language Processing |
| title | R Deep Learning Cookbook |
| title_auth | R Deep Learning Cookbook |
| title_exact_search | R Deep Learning Cookbook |
| title_exact_search_txtP | R Deep Learning Cookbook |
| title_full | R Deep Learning Cookbook Prakash, Dr. PKS |
| title_fullStr | R Deep Learning Cookbook Prakash, Dr. PKS |
| title_full_unstemmed | R Deep Learning Cookbook Prakash, Dr. PKS |
| title_short | R Deep Learning Cookbook |
| title_sort | r deep learning cookbook |
| topic | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Natural Language Processing |
| topic_facet | COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Natural Language Processing |
| work_keys_str_mv | AT prakashdrpks rdeeplearningcookbook AT raoachyutunisrikrishna rdeeplearningcookbook |