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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Zohuri, Bahman (Autor), Zadeh, Siamak (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York : Nova Science Publishers, 2020.
Colección:Computer science, technology and applications
Temas:
Artificial intelligence.
Machine learning.
Artificial Intelligence
Machine Learning
Intelligence artificielle.
Apprentissage automatique.
artificial intelligence.
Artificial intelligence
Machine learning
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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