Artificial intelligence driven by machine learning and deep learning /:
"The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York :
Nova Science Publishers,
2020.
|
| Schriftenreihe: | Computer science, technology and applications
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the BigData (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations' customers. The decision-makers need to get vital insights into the customers' actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics"-- |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 1536183679 9781536183672 |
Internformat
MARC
| LEADER | 00000cam a22000008i 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBA-on1176316636 | ||
| 003 | OCoLC | ||
| 005 | 20241004212047.0 | ||
| 006 | m o d | ||
| 007 | cr ||||||||||| | ||
| 008 | 200706s2020 nyu ob 001 0 eng | ||
| 010 | |a 2020030182 | ||
| 040 | |a DLC |b eng |e rda |e pn |c DLC |d OCLCO |d OCLCF |d EBLCP |d N$T |d YDX |d OCLCQ |d OCLCO |d K6U |d OCLCQ |d OCLCO | ||
| 020 | |a 1536183679 | ||
| 020 | |a 9781536183672 |q (electronic bk.) | ||
| 020 | |z 9781536183146 |q (hardcover) | ||
| 035 | |a (OCoLC)1176316636 | ||
| 042 | |a pcc | ||
| 050 | 0 | 0 | |a Q334.5 |
| 082 | 7 | |a 006.3 |2 23 | |
| 049 | |a MAIN | ||
| 100 | 1 | |a Zohuri, Bahman, |e author. | |
| 245 | 1 | 0 | |a Artificial intelligence driven by machine learning and deep learning / |c Bahman Zohuri, Siamak Zadeh (authors), Golden Gate University, San Francisco, CA, US. |
| 263 | |a 2010 | ||
| 264 | 1 | |a New York : |b Nova Science Publishers, |c 2020. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Computer science, technology and applications | |
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the BigData (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations' customers. The decision-makers need to get vital insights into the customers' actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics"-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher; resource not viewed. | |
| 505 | 0 | |a Intro -- Contents -- Preface -- Acknowledgment -- Chapter 1 -- Artificial Intelligence -- 1.1. Introduction -- 1.2. History of Artificial Intelligence -- 1.3. Weak Artificial Intelligence (WAI) -- 1.3.1. And It Is Indeed a Possibility. The Signs Are All There -- 1.3.2. Technological Singularity -- 1.4. Artificial General Intelligence (AGI) -- 1.4.1. Existential Risk from Artificial General Intelligence -- 1.5. Natural Language Processing (NLP) -- 1.5.1. How Does NLP Work? -- 1.6. Cognitive Science and Cognitive Linguistics -- 1.7. Big Data -- 1.7.1. Big Data History and Current Considerations | |
| 505 | 8 | |a 1.7.2. What Are Big Data and Big Data Analytics? -- 1.7.3. Why Is Big Data Important? -- 1.7.4. Where Big Data Is Used and Who Uses it -- 1.7.5. How Does Big Data Work -- References -- Chapter 2 -- Machine Learning -- 2.1. Introduction -- 2.2. Problem Solving with Machine Learning -- 2.3. Estimating Probability Distributions -- 2.4. Linear Classifiers and Perceptron Algorithm -- 2.5. Decision Trees and Model Selection -- 2.6. Random Forest and How Does It Work -- 2.7. Overfitting in Decision Trees -- 2.8. Learning with Kernel Machines and Support Vector Machines | |
| 505 | 8 | |a 2.9. Debugging and Improving Machine Learning -- 2.10. Machine Learning Logistic (MLL) -- 2.11. Why Machine Learning -- 2.12. Machine Learning Boosting eCommerce -- 2.12.1. Eight Significant Applications of Machine Learning in eCommerce -- 2.12.2. Conclusion of Machine Learning and eCommerce -- References -- Chapter 3 -- Deep Learning -- 3.1. Introduction -- 3.2. Neural Networks Three Classes (MLP, CNN and RNN) -- 3.2.1. Multi-Layer Perceptrons (MLPs) -- 3.2.1.1. When to Use Multi-Layer Perceptrons (MLPs)? -- 3.2.2. Convolutional Neural Networks (CNNs) | |
| 505 | 8 | |a 3.2.2.1. When to Use Convolutional Neural Networks (CNNs)? -- 3.2.3. Recurrent Neural Networks (RNNs) -- 3.2.3.1. Cardinality from Timesteps (Not Features!) -- 3.2.3.2. Two Common Misunderstandings by Practitioners -- 3.3. Neural Networks Prospect -- 3.4. Deep Learning and Neural Networks -- 3.5. Hybrid Network Models -- 3.6. Deep Learning versus Machine Learning -- 3.7. Deep Learning Limitation -- 3.7.1. Local Generalization versus Generalization -- 3.8. Summary -- 3.9. The Future of Deep Learning -- 3.9.1. Models as Programs -- 3.9.2. Beyond Backpropagation and Differential Layers | |
| 505 | 8 | |a 3.9.3. Automated Machine Learning -- 3.9.4. Lifelong Learning and Modular Subroutine Reuse -- 3.9.5. In Summary and the Long-Term Vision -- References -- Chapter 4 -- Neural Networks Concepts -- 4.1. Introduction -- 4.2. Artificial Neural Network (ANN) -- 4.2.1. Artificial Neuron with Continuous Characteristics -- 4.2.2. Single-Layer Network -- 4.2.3. Multilayer Network -- 4.2.4. Learning Process -- 4.3. Back-Propagation Neural Networks -- 4.3.1. Linear Separability and the XOR Problem -- 4.3.2. The Architecture of Backpropagation Networks -- 4.3.3. Back Propagation Processing Unit | |
| 650 | 0 | |a Artificial intelligence. |0 http://id.loc.gov/authorities/subjects/sh85008180 | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 2 | |a Artificial Intelligence |0 https://id.nlm.nih.gov/mesh/D001185 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Machine learning |2 fast | |
| 700 | 1 | |a Zadeh, Siamak, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Zohuri, Bahman. |t Artificial intelligence driven by machine learning and deep learning. |d New York : Nova Science Publishers, 2020 |z 9781536183146 |w (DLC) 2020030181 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2518335 |3 Volltext |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6376512 | ||
| 938 | |a EBSCOhost |b EBSC |n 2518335 | ||
| 938 | |a YBP Library Services |b YANK |n 16879029 | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBA | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-on1176316636 |
|---|---|
| _version_ | 1816882525409116160 |
| adam_text | |
| any_adam_object | |
| author | Zohuri, Bahman Zadeh, Siamak |
| author_facet | Zohuri, Bahman Zadeh, Siamak |
| author_role | aut aut |
| author_sort | Zohuri, Bahman |
| author_variant | b z bz s z sz |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | Q334 |
| callnumber-raw | Q334.5 |
| callnumber-search | Q334.5 |
| callnumber-sort | Q 3334.5 |
| callnumber-subject | Q - General Science |
| collection | ZDB-4-EBA |
| contents | Intro -- Contents -- Preface -- Acknowledgment -- Chapter 1 -- Artificial Intelligence -- 1.1. Introduction -- 1.2. History of Artificial Intelligence -- 1.3. Weak Artificial Intelligence (WAI) -- 1.3.1. And It Is Indeed a Possibility. The Signs Are All There -- 1.3.2. Technological Singularity -- 1.4. Artificial General Intelligence (AGI) -- 1.4.1. Existential Risk from Artificial General Intelligence -- 1.5. Natural Language Processing (NLP) -- 1.5.1. How Does NLP Work? -- 1.6. Cognitive Science and Cognitive Linguistics -- 1.7. Big Data -- 1.7.1. Big Data History and Current Considerations 1.7.2. What Are Big Data and Big Data Analytics? -- 1.7.3. Why Is Big Data Important? -- 1.7.4. Where Big Data Is Used and Who Uses it -- 1.7.5. How Does Big Data Work -- References -- Chapter 2 -- Machine Learning -- 2.1. Introduction -- 2.2. Problem Solving with Machine Learning -- 2.3. Estimating Probability Distributions -- 2.4. Linear Classifiers and Perceptron Algorithm -- 2.5. Decision Trees and Model Selection -- 2.6. Random Forest and How Does It Work -- 2.7. Overfitting in Decision Trees -- 2.8. Learning with Kernel Machines and Support Vector Machines 2.9. Debugging and Improving Machine Learning -- 2.10. Machine Learning Logistic (MLL) -- 2.11. Why Machine Learning -- 2.12. Machine Learning Boosting eCommerce -- 2.12.1. Eight Significant Applications of Machine Learning in eCommerce -- 2.12.2. Conclusion of Machine Learning and eCommerce -- References -- Chapter 3 -- Deep Learning -- 3.1. Introduction -- 3.2. Neural Networks Three Classes (MLP, CNN and RNN) -- 3.2.1. Multi-Layer Perceptrons (MLPs) -- 3.2.1.1. When to Use Multi-Layer Perceptrons (MLPs)? -- 3.2.2. Convolutional Neural Networks (CNNs) 3.2.2.1. When to Use Convolutional Neural Networks (CNNs)? -- 3.2.3. Recurrent Neural Networks (RNNs) -- 3.2.3.1. Cardinality from Timesteps (Not Features!) -- 3.2.3.2. Two Common Misunderstandings by Practitioners -- 3.3. Neural Networks Prospect -- 3.4. Deep Learning and Neural Networks -- 3.5. Hybrid Network Models -- 3.6. Deep Learning versus Machine Learning -- 3.7. Deep Learning Limitation -- 3.7.1. Local Generalization versus Generalization -- 3.8. Summary -- 3.9. The Future of Deep Learning -- 3.9.1. Models as Programs -- 3.9.2. Beyond Backpropagation and Differential Layers 3.9.3. Automated Machine Learning -- 3.9.4. Lifelong Learning and Modular Subroutine Reuse -- 3.9.5. In Summary and the Long-Term Vision -- References -- Chapter 4 -- Neural Networks Concepts -- 4.1. Introduction -- 4.2. Artificial Neural Network (ANN) -- 4.2.1. Artificial Neuron with Continuous Characteristics -- 4.2.2. Single-Layer Network -- 4.2.3. Multilayer Network -- 4.2.4. Learning Process -- 4.3. Back-Propagation Neural Networks -- 4.3.1. Linear Separability and the XOR Problem -- 4.3.2. The Architecture of Backpropagation Networks -- 4.3.3. Back Propagation Processing Unit |
| ctrlnum | (OCoLC)1176316636 |
| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06742cam a22006138i 4500</leader><controlfield tag="001">ZDB-4-EBA-on1176316636</controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20241004212047.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr |||||||||||</controlfield><controlfield tag="008">200706s2020 nyu ob 001 0 eng </controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a"> 2020030182</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DLC</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">DLC</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCF</subfield><subfield code="d">EBLCP</subfield><subfield code="d">N$T</subfield><subfield code="d">YDX</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">K6U</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1536183679</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781536183672</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781536183146</subfield><subfield code="q">(hardcover)</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1176316636</subfield></datafield><datafield tag="042" ind1=" " ind2=" "><subfield code="a">pcc</subfield></datafield><datafield tag="050" ind1="0" ind2="0"><subfield code="a">Q334.5</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">006.3</subfield><subfield code="2">23</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zohuri, Bahman,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Artificial intelligence driven by machine learning and deep learning /</subfield><subfield code="c">Bahman Zohuri, Siamak Zadeh (authors), Golden Gate University, San Francisco, CA, US.</subfield></datafield><datafield tag="263" ind1=" " ind2=" "><subfield code="a">2010</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York :</subfield><subfield code="b">Nova Science Publishers,</subfield><subfield code="c">2020.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Computer science, technology and applications</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">"The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the BigData (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations' customers. The decision-makers need to get vital insights into the customers' actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics"--</subfield><subfield code="c">Provided by publisher</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record and CIP data provided by publisher; resource not viewed.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Intro -- Contents -- Preface -- Acknowledgment -- Chapter 1 -- Artificial Intelligence -- 1.1. Introduction -- 1.2. History of Artificial Intelligence -- 1.3. Weak Artificial Intelligence (WAI) -- 1.3.1. And It Is Indeed a Possibility. The Signs Are All There -- 1.3.2. Technological Singularity -- 1.4. Artificial General Intelligence (AGI) -- 1.4.1. Existential Risk from Artificial General Intelligence -- 1.5. Natural Language Processing (NLP) -- 1.5.1. How Does NLP Work? -- 1.6. Cognitive Science and Cognitive Linguistics -- 1.7. Big Data -- 1.7.1. Big Data History and Current Considerations</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.7.2. What Are Big Data and Big Data Analytics? -- 1.7.3. Why Is Big Data Important? -- 1.7.4. Where Big Data Is Used and Who Uses it -- 1.7.5. How Does Big Data Work -- References -- Chapter 2 -- Machine Learning -- 2.1. Introduction -- 2.2. Problem Solving with Machine Learning -- 2.3. Estimating Probability Distributions -- 2.4. Linear Classifiers and Perceptron Algorithm -- 2.5. Decision Trees and Model Selection -- 2.6. Random Forest and How Does It Work -- 2.7. Overfitting in Decision Trees -- 2.8. Learning with Kernel Machines and Support Vector Machines</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.9. Debugging and Improving Machine Learning -- 2.10. Machine Learning Logistic (MLL) -- 2.11. Why Machine Learning -- 2.12. Machine Learning Boosting eCommerce -- 2.12.1. Eight Significant Applications of Machine Learning in eCommerce -- 2.12.2. Conclusion of Machine Learning and eCommerce -- References -- Chapter 3 -- Deep Learning -- 3.1. Introduction -- 3.2. Neural Networks Three Classes (MLP, CNN and RNN) -- 3.2.1. Multi-Layer Perceptrons (MLPs) -- 3.2.1.1. When to Use Multi-Layer Perceptrons (MLPs)? -- 3.2.2. Convolutional Neural Networks (CNNs)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.2.2.1. When to Use Convolutional Neural Networks (CNNs)? -- 3.2.3. Recurrent Neural Networks (RNNs) -- 3.2.3.1. Cardinality from Timesteps (Not Features!) -- 3.2.3.2. Two Common Misunderstandings by Practitioners -- 3.3. Neural Networks Prospect -- 3.4. Deep Learning and Neural Networks -- 3.5. Hybrid Network Models -- 3.6. Deep Learning versus Machine Learning -- 3.7. Deep Learning Limitation -- 3.7.1. Local Generalization versus Generalization -- 3.8. Summary -- 3.9. The Future of Deep Learning -- 3.9.1. Models as Programs -- 3.9.2. Beyond Backpropagation and Differential Layers</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.9.3. Automated Machine Learning -- 3.9.4. Lifelong Learning and Modular Subroutine Reuse -- 3.9.5. In Summary and the Long-Term Vision -- References -- Chapter 4 -- Neural Networks Concepts -- 4.1. Introduction -- 4.2. Artificial Neural Network (ANN) -- 4.2.1. Artificial Neuron with Continuous Characteristics -- 4.2.2. Single-Layer Network -- 4.2.3. Multilayer Network -- 4.2.4. Learning Process -- 4.3. Back-Propagation Neural Networks -- 4.3.1. Linear Separability and the XOR Problem -- 4.3.2. The Architecture of Backpropagation Networks -- 4.3.3. Back Propagation Processing Unit</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85008180</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85079324</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Artificial Intelligence</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D001185</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Machine Learning</subfield><subfield code="0">https://id.nlm.nih.gov/mesh/D000069550</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Intelligence artificielle.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Apprentissage automatique.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">artificial intelligence.</subfield><subfield code="2">aat</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Artificial intelligence</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Machine learning</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zadeh, Siamak,</subfield><subfield code="e">author.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Zohuri, Bahman.</subfield><subfield code="t">Artificial intelligence driven by machine learning and deep learning.</subfield><subfield code="d">New York : Nova Science Publishers, 2020</subfield><subfield code="z">9781536183146</subfield><subfield code="w">(DLC) 2020030181</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2518335</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ProQuest Ebook Central</subfield><subfield code="b">EBLB</subfield><subfield code="n">EBL6376512</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">2518335</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">16879029</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-on1176316636 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:00Z |
| institution | BVB |
| isbn | 1536183679 9781536183672 |
| language | English |
| lccn | 2020030182 |
| oclc_num | 1176316636 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Nova Science Publishers, |
| record_format | marc |
| series2 | Computer science, technology and applications |
| spelling | Zohuri, Bahman, author. Artificial intelligence driven by machine learning and deep learning / Bahman Zohuri, Siamak Zadeh (authors), Golden Gate University, San Francisco, CA, US. 2010 New York : Nova Science Publishers, 2020. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Computer science, technology and applications Includes bibliographical references and index. "The future of any business from banking, e-commerce, real estate, homeland security, healthcare, marketing, the stock market, manufacturing, education, retail to government organizations depends on the data and analytics capabilities that are built and scaled. The speed of change in technology in recent years has been a real challenge for all businesses. To manage that, a significant number of organizations are exploring the BigData (BD) infrastructure that helps them to take advantage of new opportunities while saving costs. Timely transformation of information is also critical for the survivability of an organization. Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment. It is no longer enough to rely on a sampling of information about the organizations' customers. The decision-makers need to get vital insights into the customers' actual behavior, which requires enormous volumes of data to be processed. We believe that Big Data infrastructure is the key to successful Artificial Intelligence (AI) deployments and accurate, unbiased real-time insights. Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL. In this article, we discuss these topics"-- Provided by publisher Print version record and CIP data provided by publisher; resource not viewed. Intro -- Contents -- Preface -- Acknowledgment -- Chapter 1 -- Artificial Intelligence -- 1.1. Introduction -- 1.2. History of Artificial Intelligence -- 1.3. Weak Artificial Intelligence (WAI) -- 1.3.1. And It Is Indeed a Possibility. The Signs Are All There -- 1.3.2. Technological Singularity -- 1.4. Artificial General Intelligence (AGI) -- 1.4.1. Existential Risk from Artificial General Intelligence -- 1.5. Natural Language Processing (NLP) -- 1.5.1. How Does NLP Work? -- 1.6. Cognitive Science and Cognitive Linguistics -- 1.7. Big Data -- 1.7.1. Big Data History and Current Considerations 1.7.2. What Are Big Data and Big Data Analytics? -- 1.7.3. Why Is Big Data Important? -- 1.7.4. Where Big Data Is Used and Who Uses it -- 1.7.5. How Does Big Data Work -- References -- Chapter 2 -- Machine Learning -- 2.1. Introduction -- 2.2. Problem Solving with Machine Learning -- 2.3. Estimating Probability Distributions -- 2.4. Linear Classifiers and Perceptron Algorithm -- 2.5. Decision Trees and Model Selection -- 2.6. Random Forest and How Does It Work -- 2.7. Overfitting in Decision Trees -- 2.8. Learning with Kernel Machines and Support Vector Machines 2.9. Debugging and Improving Machine Learning -- 2.10. Machine Learning Logistic (MLL) -- 2.11. Why Machine Learning -- 2.12. Machine Learning Boosting eCommerce -- 2.12.1. Eight Significant Applications of Machine Learning in eCommerce -- 2.12.2. Conclusion of Machine Learning and eCommerce -- References -- Chapter 3 -- Deep Learning -- 3.1. Introduction -- 3.2. Neural Networks Three Classes (MLP, CNN and RNN) -- 3.2.1. Multi-Layer Perceptrons (MLPs) -- 3.2.1.1. When to Use Multi-Layer Perceptrons (MLPs)? -- 3.2.2. Convolutional Neural Networks (CNNs) 3.2.2.1. When to Use Convolutional Neural Networks (CNNs)? -- 3.2.3. Recurrent Neural Networks (RNNs) -- 3.2.3.1. Cardinality from Timesteps (Not Features!) -- 3.2.3.2. Two Common Misunderstandings by Practitioners -- 3.3. Neural Networks Prospect -- 3.4. Deep Learning and Neural Networks -- 3.5. Hybrid Network Models -- 3.6. Deep Learning versus Machine Learning -- 3.7. Deep Learning Limitation -- 3.7.1. Local Generalization versus Generalization -- 3.8. Summary -- 3.9. The Future of Deep Learning -- 3.9.1. Models as Programs -- 3.9.2. Beyond Backpropagation and Differential Layers 3.9.3. Automated Machine Learning -- 3.9.4. Lifelong Learning and Modular Subroutine Reuse -- 3.9.5. In Summary and the Long-Term Vision -- References -- Chapter 4 -- Neural Networks Concepts -- 4.1. Introduction -- 4.2. Artificial Neural Network (ANN) -- 4.2.1. Artificial Neuron with Continuous Characteristics -- 4.2.2. Single-Layer Network -- 4.2.3. Multilayer Network -- 4.2.4. Learning Process -- 4.3. Back-Propagation Neural Networks -- 4.3.1. Linear Separability and the XOR Problem -- 4.3.2. The Architecture of Backpropagation Networks -- 4.3.3. Back Propagation Processing Unit Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat Artificial intelligence fast Machine learning fast Zadeh, Siamak, author. Print version: Zohuri, Bahman. Artificial intelligence driven by machine learning and deep learning. New York : Nova Science Publishers, 2020 9781536183146 (DLC) 2020030181 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2518335 Volltext |
| spellingShingle | Zohuri, Bahman Zadeh, Siamak Artificial intelligence driven by machine learning and deep learning / Intro -- Contents -- Preface -- Acknowledgment -- Chapter 1 -- Artificial Intelligence -- 1.1. Introduction -- 1.2. History of Artificial Intelligence -- 1.3. Weak Artificial Intelligence (WAI) -- 1.3.1. And It Is Indeed a Possibility. The Signs Are All There -- 1.3.2. Technological Singularity -- 1.4. Artificial General Intelligence (AGI) -- 1.4.1. Existential Risk from Artificial General Intelligence -- 1.5. Natural Language Processing (NLP) -- 1.5.1. How Does NLP Work? -- 1.6. Cognitive Science and Cognitive Linguistics -- 1.7. Big Data -- 1.7.1. Big Data History and Current Considerations 1.7.2. What Are Big Data and Big Data Analytics? -- 1.7.3. Why Is Big Data Important? -- 1.7.4. Where Big Data Is Used and Who Uses it -- 1.7.5. How Does Big Data Work -- References -- Chapter 2 -- Machine Learning -- 2.1. Introduction -- 2.2. Problem Solving with Machine Learning -- 2.3. Estimating Probability Distributions -- 2.4. Linear Classifiers and Perceptron Algorithm -- 2.5. Decision Trees and Model Selection -- 2.6. Random Forest and How Does It Work -- 2.7. Overfitting in Decision Trees -- 2.8. Learning with Kernel Machines and Support Vector Machines 2.9. Debugging and Improving Machine Learning -- 2.10. Machine Learning Logistic (MLL) -- 2.11. Why Machine Learning -- 2.12. Machine Learning Boosting eCommerce -- 2.12.1. Eight Significant Applications of Machine Learning in eCommerce -- 2.12.2. Conclusion of Machine Learning and eCommerce -- References -- Chapter 3 -- Deep Learning -- 3.1. Introduction -- 3.2. Neural Networks Three Classes (MLP, CNN and RNN) -- 3.2.1. Multi-Layer Perceptrons (MLPs) -- 3.2.1.1. When to Use Multi-Layer Perceptrons (MLPs)? -- 3.2.2. Convolutional Neural Networks (CNNs) 3.2.2.1. When to Use Convolutional Neural Networks (CNNs)? -- 3.2.3. Recurrent Neural Networks (RNNs) -- 3.2.3.1. Cardinality from Timesteps (Not Features!) -- 3.2.3.2. Two Common Misunderstandings by Practitioners -- 3.3. Neural Networks Prospect -- 3.4. Deep Learning and Neural Networks -- 3.5. Hybrid Network Models -- 3.6. Deep Learning versus Machine Learning -- 3.7. Deep Learning Limitation -- 3.7.1. Local Generalization versus Generalization -- 3.8. Summary -- 3.9. The Future of Deep Learning -- 3.9.1. Models as Programs -- 3.9.2. Beyond Backpropagation and Differential Layers 3.9.3. Automated Machine Learning -- 3.9.4. Lifelong Learning and Modular Subroutine Reuse -- 3.9.5. In Summary and the Long-Term Vision -- References -- Chapter 4 -- Neural Networks Concepts -- 4.1. Introduction -- 4.2. Artificial Neural Network (ANN) -- 4.2.1. Artificial Neuron with Continuous Characteristics -- 4.2.2. Single-Layer Network -- 4.2.3. Multilayer Network -- 4.2.4. Learning Process -- 4.3. Back-Propagation Neural Networks -- 4.3.1. Linear Separability and the XOR Problem -- 4.3.2. The Architecture of Backpropagation Networks -- 4.3.3. Back Propagation Processing Unit Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat Artificial intelligence fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh85079324 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Artificial intelligence driven by machine learning and deep learning / |
| title_auth | Artificial intelligence driven by machine learning and deep learning / |
| title_exact_search | Artificial intelligence driven by machine learning and deep learning / |
| title_full | Artificial intelligence driven by machine learning and deep learning / Bahman Zohuri, Siamak Zadeh (authors), Golden Gate University, San Francisco, CA, US. |
| title_fullStr | Artificial intelligence driven by machine learning and deep learning / Bahman Zohuri, Siamak Zadeh (authors), Golden Gate University, San Francisco, CA, US. |
| title_full_unstemmed | Artificial intelligence driven by machine learning and deep learning / Bahman Zohuri, Siamak Zadeh (authors), Golden Gate University, San Francisco, CA, US. |
| title_short | Artificial intelligence driven by machine learning and deep learning / |
| title_sort | artificial intelligence driven by machine learning and deep learning |
| topic | Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat Artificial intelligence fast Machine learning fast |
| topic_facet | Artificial intelligence. Machine learning. Artificial Intelligence Machine Learning Intelligence artificielle. Apprentissage automatique. artificial intelligence. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2518335 |
| work_keys_str_mv | AT zohuribahman artificialintelligencedrivenbymachinelearninganddeeplearning AT zadehsiamak artificialintelligencedrivenbymachinelearninganddeeplearning |