Hands-on deep learning for IoT: train neural network models to develop intelligent IoT applications
Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amount...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, Mumbai
Packt Publishing
2019
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Zusammenfassung: | Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | 298 Seiten Illustrationen |
| ISBN: | 1789616131 9781789616132 |
Internformat
MARC
| LEADER | 00000nam a22000001c 4500 | ||
|---|---|---|---|
| 001 | BV046839667 | ||
| 003 | DE-604 | ||
| 007 | t| | ||
| 008 | 200805s2019 xx a||| |||| 00||| eng d | ||
| 020 | |a 1789616131 |9 1-78961-613-1 | ||
| 020 | |a 9781789616132 |9 978-1-78961-613-2 | ||
| 035 | |a (OCoLC)1193305014 | ||
| 035 | |a (DE-599)BVBBV046839667 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-573 | ||
| 082 | 0 | |a 004.678 | |
| 084 | |a ST 205 |0 (DE-625)143613: |2 rvk | ||
| 100 | 1 | |a Razzaque, Mohammed Abdur |e Verfasser |0 (DE-588)170037045 |4 aut | |
| 245 | 1 | 0 | |a Hands-on deep learning for IoT |b train neural network models to develop intelligent IoT applications |c Mohammad Abdur Razzaque, Md. Rezaul Karim |
| 250 | |a 1st ed | ||
| 264 | 1 | |a Birmingham, Mumbai |b Packt Publishing |c 2019 | |
| 264 | 4 | |c © 2019 | |
| 300 | |a 298 Seiten |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 520 | 3 | |a Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making | |
| 650 | 0 | 7 | |a Internet der Dinge |0 (DE-588)7713781-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Deep learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Big Data and Business Intellig | |
| 655 | 7 | |0 (DE-588)4123623-3 |a Lehrbuch |2 gnd-content | |
| 689 | 0 | 0 | |a Internet der Dinge |0 (DE-588)7713781-4 |D s |
| 689 | 0 | 1 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | 2 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Karim, Md. Rezaul |d 1965- |e Verfasser |0 (DE-588)1196366179 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-78961-606-4 |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-032248663 | |
Datensatz im Suchindex
| _version_ | 1818330291732742144 |
|---|---|
| adam_text | |
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Razzaque, Mohammed Abdur Karim, Md. Rezaul 1965- |
| author_GND | (DE-588)170037045 (DE-588)1196366179 |
| author_facet | Razzaque, Mohammed Abdur Karim, Md. Rezaul 1965- |
| author_role | aut aut |
| author_sort | Razzaque, Mohammed Abdur |
| author_variant | m a r ma mar m r k mr mrk |
| building | Verbundindex |
| bvnumber | BV046839667 |
| classification_rvk | ST 205 |
| ctrlnum | (OCoLC)1193305014 (DE-599)BVBBV046839667 |
| dewey-full | 004.678 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004.678 |
| dewey-search | 004.678 |
| dewey-sort | 14.678 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | 1st ed |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a22000001c 4500</leader><controlfield tag="001">BV046839667</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">200805s2019 xx a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1789616131</subfield><subfield code="9">1-78961-613-1</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781789616132</subfield><subfield code="9">978-1-78961-613-2</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1193305014</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV046839667</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="049" ind1=" " ind2=" "><subfield code="a">DE-573</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004.678</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 205</subfield><subfield code="0">(DE-625)143613:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Razzaque, Mohammed Abdur</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)170037045</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Hands-on deep learning for IoT</subfield><subfield code="b">train neural network models to develop intelligent IoT applications</subfield><subfield code="c">Mohammad Abdur Razzaque, Md. Rezaul Karim</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham, Mumbai</subfield><subfield code="b">Packt Publishing</subfield><subfield code="c">2019</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">298 Seiten</subfield><subfield code="b">Illustrationen</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">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Internet der Dinge</subfield><subfield code="0">(DE-588)7713781-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Big Data and Business Intellig</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4123623-3</subfield><subfield code="a">Lehrbuch</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Internet der Dinge</subfield><subfield code="0">(DE-588)7713781-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Neuronales Netz</subfield><subfield code="0">(DE-588)4226127-2</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Deep learning</subfield><subfield code="0">(DE-588)1135597375</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Karim, Md. Rezaul</subfield><subfield code="d">1965-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1196366179</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-78961-606-4</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032248663</subfield></datafield></record></collection> |
| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV046839667 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:07:37Z |
| indexdate | 2024-12-13T13:01:37Z |
| institution | BVB |
| isbn | 1789616131 9781789616132 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032248663 |
| oclc_num | 1193305014 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | 298 Seiten Illustrationen |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Packt Publishing |
| record_format | marc |
| spelling | Razzaque, Mohammed Abdur Verfasser (DE-588)170037045 aut Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications Mohammad Abdur Razzaque, Md. Rezaul Karim 1st ed Birmingham, Mumbai Packt Publishing 2019 © 2019 298 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You’ll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You’ll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you’ll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making Internet der Dinge (DE-588)7713781-4 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Big Data and Business Intellig (DE-588)4123623-3 Lehrbuch gnd-content Internet der Dinge (DE-588)7713781-4 s Neuronales Netz (DE-588)4226127-2 s Deep learning (DE-588)1135597375 s DE-604 Karim, Md. Rezaul 1965- Verfasser (DE-588)1196366179 aut Erscheint auch als Online-Ausgabe 978-1-78961-606-4 |
| spellingShingle | Razzaque, Mohammed Abdur Karim, Md. Rezaul 1965- Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications Internet der Dinge (DE-588)7713781-4 gnd Neuronales Netz (DE-588)4226127-2 gnd Deep learning (DE-588)1135597375 gnd |
| subject_GND | (DE-588)7713781-4 (DE-588)4226127-2 (DE-588)1135597375 (DE-588)4123623-3 |
| title | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications |
| title_auth | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications |
| title_exact_search | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications |
| title_exact_search_txtP | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications |
| title_full | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications Mohammad Abdur Razzaque, Md. Rezaul Karim |
| title_fullStr | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications Mohammad Abdur Razzaque, Md. Rezaul Karim |
| title_full_unstemmed | Hands-on deep learning for IoT train neural network models to develop intelligent IoT applications Mohammad Abdur Razzaque, Md. Rezaul Karim |
| title_short | Hands-on deep learning for IoT |
| title_sort | hands on deep learning for iot train neural network models to develop intelligent iot applications |
| title_sub | train neural network models to develop intelligent IoT applications |
| topic | Internet der Dinge (DE-588)7713781-4 gnd Neuronales Netz (DE-588)4226127-2 gnd Deep learning (DE-588)1135597375 gnd |
| topic_facet | Internet der Dinge Neuronales Netz Deep learning Lehrbuch |
| work_keys_str_mv | AT razzaquemohammedabdur handsondeeplearningforiottrainneuralnetworkmodelstodevelopintelligentiotapplications AT karimmdrezaul handsondeeplearningforiottrainneuralnetworkmodelstodevelopintelligentiotapplications |