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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Nova Science Publishers,
2020.
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| Colección: | Computer science, technology and applications
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a22000008i 4500 | ||
|---|---|---|---|
| 001 | EBSCO_on1176316636 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
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| 008 | 200706s2020 nyu ob 001 0 eng | ||
| 010 | |a 2020030182 | ||
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| 020 | |a 1536183679 | ||
| 020 | |a 9781536183672 |q (electronic bk.) | ||
| 020 | |z 9781536183146 |q (hardcover) | ||
| 035 | |a (OCoLC)1176316636 | ||
| 042 | |a pcc | ||
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| 082 | 0 | 0 | |a 006.3 |2 23 |
| 049 | |a UAMI | ||
| 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 | |
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 2 | |a Machine Learning | |
| 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 |
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