Advanced deep learning with TensorFlow 2 and Keras: apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more
Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and se...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
February 2020
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | UBY01 UER01 |
| Zusammenfassung: | Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel.. |
| Beschreibung: | 1 Online-Ressource (xiii, 491 Seiten) |
| ISBN: | 9781838825720 |
Internformat
MARC
| LEADER | 00000nmm a22000001c 4500 | ||
|---|---|---|---|
| 001 | BV046743498 | ||
| 003 | DE-604 | ||
| 005 | 20211207 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 200602s2020 |||| o||u| ||||||eng d | ||
| 020 | |a 9781838825720 |c Online |9 9781838825720 | ||
| 035 | |a (ZDB-30-PQE)EBC6126530 | ||
| 035 | |a (OCoLC)1164632642 | ||
| 035 | |a (DE-599)BVBBV046743498 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-706 |a DE-29 | ||
| 084 | |a ST 301 |0 (DE-625)143651: |2 rvk | ||
| 100 | 1 | |a Atienza, Rowel |e Verfasser |0 (DE-588)1182346979 |4 aut | |
| 245 | 1 | 0 | |a Advanced deep learning with TensorFlow 2 and Keras |b apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more |c Atienza, Rowel |
| 250 | |a Second edition | ||
| 264 | 1 | |a Birmingham ; Mumbai |b Packt |c February 2020 | |
| 300 | |a 1 Online-Ressource (xiii, 491 Seiten) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 520 | 3 | |a Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. | |
| 520 | 3 | |a Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. | |
| 520 | 3 | |a What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel.. | |
| 650 | 0 | 7 | |a TensorFlow |0 (DE-588)1153577011 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Deep learning |0 (DE-588)1135597375 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Keras |g Framework, Informatik |0 (DE-588)1160521077 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Electronic books ; local | |
| 689 | 0 | 0 | |a Deep learning |0 (DE-588)1135597375 |D s |
| 689 | 0 | 1 | |a Keras |g Framework, Informatik |0 (DE-588)1160521077 |D s |
| 689 | 0 | 2 | |a TensorFlow |0 (DE-588)1153577011 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 978-1-83882-165-4 |
| 912 | |a ZDB-4-NLEBK |a ZDB-30-PQE |a ZDB-5-WPSE | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032153354 | ||
| 966 | e | |u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2384229 |l UBY01 |p ZDB-4-NLEBK |q UBY01_DDA20 |x Aggregator |3 Volltext | |
| 966 | e | |u https://ebookcentral.proquest.com/lib/erlangen/detail.action?docID=6126530 |l UER01 |p ZDB-30-PQE |q UER_PDA_PQE_Kauf |x Aggregator |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804181497555451904 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Atienza, Rowel |
| author_GND | (DE-588)1182346979 |
| author_facet | Atienza, Rowel |
| author_role | aut |
| author_sort | Atienza, Rowel |
| author_variant | r a ra |
| building | Verbundindex |
| bvnumber | BV046743498 |
| classification_rvk | ST 301 |
| collection | ZDB-4-NLEBK ZDB-30-PQE ZDB-5-WPSE |
| ctrlnum | (ZDB-30-PQE)EBC6126530 (OCoLC)1164632642 (DE-599)BVBBV046743498 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | Second edition |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04424nmm a22004691c 4500</leader><controlfield tag="001">BV046743498</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20211207 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">200602s2020 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781838825720</subfield><subfield code="c">Online</subfield><subfield code="9">9781838825720</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC6126530</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1164632642</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV046743498</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-706</subfield><subfield code="a">DE-29</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 301</subfield><subfield code="0">(DE-625)143651:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Atienza, Rowel</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1182346979</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Advanced deep learning with TensorFlow 2 and Keras</subfield><subfield code="b">apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more</subfield><subfield code="c">Atienza, Rowel</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Birmingham ; Mumbai</subfield><subfield code="b">Packt</subfield><subfield code="c">February 2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xiii, 491 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="3" ind2=" "><subfield code="a">Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. </subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. </subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel..</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">TensorFlow</subfield><subfield code="0">(DE-588)1153577011</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="650" ind1="0" ind2="7"><subfield code="a">Keras</subfield><subfield code="g">Framework, Informatik</subfield><subfield code="0">(DE-588)1160521077</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Electronic books ; local</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><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="1"><subfield code="a">Keras</subfield><subfield code="g">Framework, Informatik</subfield><subfield code="0">(DE-588)1160521077</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">TensorFlow</subfield><subfield code="0">(DE-588)1153577011</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-1-83882-165-4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-NLEBK</subfield><subfield code="a">ZDB-30-PQE</subfield><subfield code="a">ZDB-5-WPSE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032153354</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2384229</subfield><subfield code="l">UBY01</subfield><subfield code="p">ZDB-4-NLEBK</subfield><subfield code="q">UBY01_DDA20</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/erlangen/detail.action?docID=6126530</subfield><subfield code="l">UER01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">UER_PDA_PQE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV046743498 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:40:03Z |
| indexdate | 2024-07-10T08:52:36Z |
| institution | BVB |
| isbn | 9781838825720 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032153354 |
| oclc_num | 1164632642 |
| open_access_boolean | |
| owner | DE-706 DE-29 |
| owner_facet | DE-706 DE-29 |
| physical | 1 Online-Ressource (xiii, 491 Seiten) |
| psigel | ZDB-4-NLEBK ZDB-30-PQE ZDB-5-WPSE ZDB-4-NLEBK UBY01_DDA20 ZDB-30-PQE UER_PDA_PQE_Kauf |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Packt |
| record_format | marc |
| spelling | Atienza, Rowel Verfasser (DE-588)1182346979 aut Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more Atienza, Rowel Second edition Birmingham ; Mumbai Packt February 2020 1 Online-Ressource (xiii, 491 Seiten) txt rdacontent c rdamedia cr rdacarrier Updated and revised second edition of the bestselling guide to advanced deep learning with TensorFlow 2 and Keras Key Features Explore the most advanced deep learning techniques that drive modern AI results New coverage of unsupervised deep learning using mutual information, object detection, and semantic segmentation Completely updated for TensorFlow 2.x Book Description Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance. Next, you'll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You'll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI. What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detection Learn the building blocks for advanced techniques - MLPss, CNN, and RNNs Understand deep neural networks - including ResNet and DenseNet Understand and build autoregressive models – autoencoders, VAEs, and GANs Discover and implement deep reinforcement learning methods Who this book is for This is not an introductory book, so fluency with Python is required. The reader should also be familiar with some machine learning approaches, and practical experience with DL will also be hel.. TensorFlow (DE-588)1153577011 gnd rswk-swf Deep learning (DE-588)1135597375 gnd rswk-swf Keras Framework, Informatik (DE-588)1160521077 gnd rswk-swf Electronic books ; local Deep learning (DE-588)1135597375 s Keras Framework, Informatik (DE-588)1160521077 s TensorFlow (DE-588)1153577011 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-83882-165-4 |
| spellingShingle | Atienza, Rowel Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more TensorFlow (DE-588)1153577011 gnd Deep learning (DE-588)1135597375 gnd Keras Framework, Informatik (DE-588)1160521077 gnd |
| subject_GND | (DE-588)1153577011 (DE-588)1135597375 (DE-588)1160521077 |
| title | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more |
| title_auth | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more |
| title_exact_search | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more |
| title_exact_search_txtP | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more |
| title_full | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more Atienza, Rowel |
| title_fullStr | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more Atienza, Rowel |
| title_full_unstemmed | Advanced deep learning with TensorFlow 2 and Keras apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more Atienza, Rowel |
| title_short | Advanced deep learning with TensorFlow 2 and Keras |
| title_sort | advanced deep learning with tensorflow 2 and keras apply dl gans vaes deep rl unsupervised learning object detection and segmentation and more |
| title_sub | apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more |
| topic | TensorFlow (DE-588)1153577011 gnd Deep learning (DE-588)1135597375 gnd Keras Framework, Informatik (DE-588)1160521077 gnd |
| topic_facet | TensorFlow Deep learning Keras Framework, Informatik |
| work_keys_str_mv | AT atienzarowel advanceddeeplearningwithtensorflow2andkerasapplydlgansvaesdeeprlunsupervisedlearningobjectdetectionandsegmentationandmore |