Artificial Intelligence Series

What Is Feedforward Neural Networks A feedforward neural network, often known as a FNN, is a type of artificial neural network that does not have connections that form a cycle between its nodes. Therefore, it is distinct from its offspring, which are known as recurrent neural networks. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Feedforward neural network Chapter 2: Artificial neural network Chapter 3: Perceptron Chapter 4: Artificial neuron Chapter 5: Multilayer perceptron Chapter 6: Delta rule Chapter 7: Backpropagation Chapter 8: Types of artificial neural networks Chapter 9: Learning rule Chapter 10: Mathematics of artificial neural networks (II) Answering the public top questions about feedforward neural networks. (III) Real world examples for the usage of feedforward neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of feedforward neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Backpropagation Backpropagation is a technique for machine learning that uses a backward pass to update the model's parameters. The goal of the algorithm is to reduce the mean squared error (MSE) as much as possible. The following actions are taken during backpropagation in a network with a single layer:Follow the path through the network from the input all the way to the output by computing the output of the hidden layers as well as the output layer. [This Is the Step of Feedforward]Calculate the derivative of the cost function with respect to the input layer and the hidden layers using the information available in the output layer.Repeatedly update the weights until they converge or sufficient iterations have been applied to the model, whichever comes first. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Backpropagation Chapter 2: Chain rule Chapter 3: Perceptron Chapter 4: Artificial neuron Chapter 5: Total derivative Chapter 6: Delta rule Chapter 7: Feedforward neural network Chapter 8: Multilayer perceptron Chapter 9: Vanishing gradient problem Chapter 10: Mathematics of artificial neural networks (II) Answering the public top questions about backpropagation. (III) Real world examples for the usage of backpropagation in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of backpropagation. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Hebbian Learning The Hebbian theory is a neuropsychological theory that asserts that an improvement in synaptic efficacy results from the repetitive and persistent stimulation of a postsynaptic cell by a presynaptic cell. This is an effort to explain synaptic plasticity, which refers to the process through which neurons in the brain change in response to learning. It was first presented in Donald Hebb's book titled The Organization of Behavior, which was published in 1949. Hebb's rule, Hebb's postulate, and the cell assembly hypothesis are all names for the same body of thought. The way that Hebb expresses it is as follows: Let us assume that the persistence or repetition of a reverberatory action tends to create long-lasting cellular modifications that add to its stability. ... When an axon of cell A is close enough to excite a cell B and takes part in firing it repeatedly or consistently, a growth process or metabolic change takes occur in one or both of the cells, which results in an increase in cell A's efficiency as one of the cells firing cell B. This can happen in either cell. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Hebbian theory Chapter 2: Chemical synapse Chapter 3: Long-term potentiation Chapter 4: Synaptic plasticity Chapter 5: Long-term depression Chapter 6: Spike-timing-dependent plasticity Chapter 7: Neural circuit Chapter 8: Metaplasticity Chapter 9: Oja's rule Chapter 10: BCM theory (II) Answering the public top questions about hebbian learning. (III) Real world examples for the usage of hebbian learning in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of hebbian learning. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Restricted Boltzmann Machine A restricted Boltzmann machine, often known as an RBM, is an example of an artificial neural network that is stochastic and generative and has the ability to develop a probability distribution over its own set of inputs. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Restricted Boltzmann Machine Chapter 2: Boltzmann Distribution Chapter 3: Entropy (Information Theory) Chapter 4: Unsupervised Learning Chapter 5: Mutual Information Chapter 6: Boltzmann Machine Chapter 7: Cross Entropy Chapter 8: Softmax Function Chapter 9: Autoencoder Chapter 10: Deep Belief Network (II) Answering the public top questions about restricted boltzmann machine. (III) Real world examples for the usage of restricted boltzmann machine in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of restricted boltzmann machine. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Perceptrons The perceptron is a technique for supervised learning of binary classifiers that is used in the field of machine learning. A function known as a binary classifier is one that can determine whether or not an input, which is often portrayed by a vector of numbers, is a member of a particular category. It is a kind of linear classifier, which means that it is a classification method that forms its predictions on the basis of a linear predictor function by combining a set of weights with the feature vector. In other words, it creates its predictions based on a linear predictor function. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Perceptron Chapter 2: Supervised learning Chapter 3: Support vector machine Chapter 4: Linear classifier Chapter 5: Pattern recognition Chapter 6: Artificial neuron Chapter 7: Hopfield network Chapter 8: Backpropagation Chapter 9: Feedforward neural network Chapter 10: Multilayer perceptron (II) Answering the public top questions about perceptrons. (III) Real world examples for the usage of perceptrons in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of perceptrons. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Multilayer Perceptron A fully connected class of feedforward artificial neural network (ANN), a multilayer perceptron, or MLP, is referred to as a multilayer perceptron. The word "MLP" is used in a way that is rather vague. Sometimes it is used to refer to any feedforward ANN, and other times it is used more specifically to refer to networks that are constructed of several layers of perceptrons; for more information, see "Terminology." When they just contain one hidden layer, multilayer perceptrons are sometimes jokingly referred to as "vanilla" neural networks. This is especially true when the term is used in a slang context. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Multilayer Perceptron Chapter 2: Artificial Neural Network Chapter 3: Perceptron Chapter 4: Artificial Neuron Chapter 5: Activation Function Chapter 6: Backpropagation Chapter 7: Delta Rule Chapter 8: Feedforward Neural Network Chapter 9: Universal Approximation Theorem Chapter 10: Mathematics of Artificial Neural Networks (II) Answering the public top questions about multilayer perceptron. (III) Real world examples for the usage of multilayer perceptron in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of multilayer perceptron. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Artificial Neural Networks Computing systems that are inspired by the biological neural networks that make up animal brains are called artificial neural networks (ANNs). These systems are more commonly referred to as neural networks (NNs) or neural nets. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Artificial neural network Chapter 2: Artificial neuron Chapter 3: Unsupervised learning Chapter 4: Backpropagation Chapter 5: Types of artificial neural networks Chapter 6: Deep learning Chapter 7: Convolutional neural network Chapter 8: Long short-term memory Chapter 9: Recurrent neural network Chapter 10: History of artificial neural networks (II) Answering the public top questions about artificial neural networks. (III) Real world examples for the usage of artificial neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Embodied Cognition Embodied cognition is a hypothesis that many facets of cognition, whether human or another, are molded by aspects of an organism's entire body. This theory can be applied to both humans and other organisms. Many researchers believe that the sensory and motor systems are fundamentally intertwined with cognitive processing. High-level mental constructs and performance across a variety of cognitive activities are both included in the cognitive characteristics. The motor system, the perceptual system, the physical interactions with the environment (situatedness), and the assumptions about the world that are built into the functional structure of the organism are all considered to be part of the corporeal aspects. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Embodied cognition Chapter 2: Cognitive science Chapter 3: Cognition Chapter 4: Situated cognition Chapter 5: Embodied cognitive science Chapter 6: Enactivism Chapter 7: Motor cognition Chapter 8: Common coding theory Chapter 9: Embodied bilingual language Chapter 10: Social cognitive neuroscience (II) Answering the public top questions about embodied cognition. (III) Real world examples for the usage of embodied cognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of embodied cognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of embodied cognition.

What Is Recurrent Neural Networks An artificial neural network that belongs to the class known as recurrent neural networks (RNNs) is one in which the connections between its nodes can form a cycle. This allows the output of some nodes to have an effect on subsequent input to the very same nodes. Because of this, it is able to display temporally dynamic behavior. RNNs are a descendant of feedforward neural networks and have the ability to use their internal state (memory) to process input sequences of varying lengths. Because of this, they are suitable for applications such as speech recognition and unsegmented, connected handwriting recognition. Theoretically, recurrent neural networks are considered to be Turing complete since they are able to execute arbitrary algorithms and interpret arbitrary sequences of inputs. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Recurrent neural network Chapter 2: Artificial neural network Chapter 3: Backpropagation Chapter 4: Long short-term memory Chapter 5: Types of artificial neural networks Chapter 6: Deep learning Chapter 7: Vanishing gradient problem Chapter 8: Bidirectional recurrent neural networks Chapter 9: Gated recurrent unit Chapter 10: Attention (machine learning) (II) Answering the public top questions about recurrent neural networks. (III) Real world examples for the usage of recurrent neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of recurrent neural networks. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Radial Basis Networks A radial basis function network is a type of artificial neural network that is used in the field of mathematical modeling. This type of network employs radial basis functions as its activation functions. The output of the network is a linear combination of the neuron parameters and the radial basis functions of the inputs to the network. Radial basis function networks have a wide range of applications, some of which include the approximation of functions, the prediction of time series, the classification of data, and the control of systems. In their study from 1988, Broomhead and Lowe, who were both researchers at the Royal Signals and Radar Establishment, were the ones who initially formulated the ideas. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Radial basis function network Chapter 2: Gradient Chapter 3: Radial basis function Chapter 4: Radial basis function kernel Chapter 5: Functional derivative Chapter 6: Jacobian matrix and determinant Chapter 7: Laplace's equation Chapter 8: Laplace operator Chapter 9: Multiple integral Chapter 10: Polyharmonic spline (II) Answering the public top questions about radial basis networks. (III) Real world examples for the usage of radial basis networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of radial basis networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Competitive Learning In artificial neural networks, competitive learning is a type of unsupervised learning in which nodes fight for the right to respond to a subset of the input data. This type of learning is known as "competitive learning." Competitive learning is a form of learning that is similar to Hebbian learning. It operates by raising the level of specialization at each node in the network. It works quite well for discovering clusters hidden within data. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Competitive Learning Chapter 2: Self-organizing map Chapter 3: Perceptron Chapter 4: Unsupervised Learning Chapter 5: Hebbian Theory Chapter 6: Backpropagation Chapter 7: Multilayer Perceptron Chapter 8: Learning Rule Chapter 9: Feature Learning Chapter 10: Types of artificial neural networks (II) Answering the public top questions about competitive learning. (III) Real world examples for the usage of competitive learning in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of competitive learning. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Group Method of Data Handling The Group Method of Data Handling (GMDH) is a series of inductive algorithms for the computer-based mathematical modeling of multi-parametric datasets that incorporates fully automatic structural and parametric optimization of models. These algorithms are used in the Group Method of Data Handling (GMDH). How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Group Method of Data Handling Chapter 2: Supervised Learning Chapter 3: Artificial Neural Network Chapter 4: Machine Learning Chapter 5: Perceptron Chapter 6: Alexey Ivakhnenko Chapter 7: Multilayer Perceptron Chapter 8: Minimum Description Length Chapter 9: Nonlinear System Identification Chapter 10: Types of Artificial Neural Networks (II) Answering the public top questions about group method of data handling. (III) Real world examples for the usage of group method of data handling in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of group method of data handling. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Convolutional Neural Networks In the field of deep learning, a convolutional neural network, also known as a CNN, is a type of artificial neural network that is typically used to conduct analysis on visual data. At least one of the layers in a CNN substitutes the mathematical operation of convolution, sometimes known as convolving, for the more traditional matrix multiplication. They are utilized in both the image recognition and processing processes, as their primary purpose is the processing of pixel data. Applications can be found in areas such as image and video recognition, recommender systems, and more.image classification,image segmentation,image analysis for medical purposes,natural language processing,interfaces between the human brain and computers, andfinance time series. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Convolutional Neural Network Chapter 2: Artificial Neural Network Chapter 3: Types of Artificial Neural Networks Chapter 4: Deep Learning Chapter 5: Activation Function Chapter 6: Layer (Deep Learning) Chapter 7: LeNet Chapter 8: Tensor (Machine Learning) Chapter 9: Receptive Field Chapter 10: History of Artificial Neural Networks (II) Answering the public top questions about convolutional neural networks. (III) Real world examples for the usage of convolutional neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of convolutional neural networks. What Is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Long Short Term Memory Long short-term memory, often known as LSTM, is a type of artificial neural network that is utilized in the domains of deep learning and artificial intelligence. LSTM neural networks have feedback connections, in contrast to more traditional feedforward neural networks. This type of recurrent neural network, commonly known as an RNN, is capable of processing not only individual data points but also complete data sequences. Because of this property, LSTM networks are particularly well-suited for the processing and forecasting of data. For instance, LSTM can be used to perform tasks such as connected unsegmented handwriting identification, speech recognition, machine translation, speech activity detection, robot control, video game development, and healthcare. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Long short-term memory Chapter 2: Artificial neural network Chapter 3: Jürgen Schmidhuber Chapter 4: Recurrent neural network Chapter 5: Vanishing gradient problem Chapter 6: Sepp Hochreiter Chapter 7: Gated recurrent unit Chapter 8: Deep learning Chapter 9: Types of artificial neural networks Chapter 10: History of artificial neural networks (II) Answering the public top questions about long short term memory. (III) Real world examples for the usage of long short term memory in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of long short term memory. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What is Hopfield Networks John Hopfield popularized the Hopfield network in 1982. It is a type of recurrent artificial neural network and a spin glass system. The Hopfield network was initially defined by Shun'ichi Amari in 1972 and by Little in 1974. The Hopfield network is based on the collaboration of Ernst Ising and Wilhelm Lenz on the Ising model. Hopfield networks are content-addressable ("associative") memory systems that can either have continuous variables or binary threshold nodes. Additionally, hopfield networks serve as a model for comprehending the human memory. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Hopfield Network Chapter 2: Unsupervised Learning Chapter 3: Ising Model Chapter 4: Hebbian Theory Chapter 5: Boltzmann Machine Chapter 6: Backpropagation Chapter 7: Multilayer Perceptron Chapter 8: Quantum Neural Network Chapter 9: Autoencoder Chapter 10: Modern Hopfield Network (II) Answering the public top questions about hopfield networks. (III) Real world examples for the usage of hopfield networks in many fields. Who This Book is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of hopfield networks. What is Artificial Intelligence Series The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Networked Control System A control system that closes its control loops through the use of a communication network is referred to as a networked control system, or NCS for short. The fact that control and feedback signals are passed between the various components of an NCS in the form of information packages and transmitted over a network is the defining characteristic of this type of control system. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Networked control system Chapter 2: Distributed control system Chapter 3: Model predictive control Chapter 4: Process automation system Chapter 5: Building automation Chapter 6: Profinet Chapter 7: EtherCAT Chapter 8: Control reconfiguration Chapter 9: Hardware-in-the-loop simulation Chapter 10: Internet of things (II) Answering the public top questions about networked control system. (III) Real world examples for the usage of networked control system in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of networked control system' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of networked control system.

What Is Situated Artificial Intelligence The term "situated" is used in the fields of artificial intelligence and cognitive science to refer to an agent that is immersed in an environment. It is usual practice to use the term "situated" to refer to robots; however, some researchers contend that software agents can also be situated if the following conditions are met: they must exist in a dynamic environment, which they can control or alter through their activities, and which they must be able to feel or perceive. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Situated Chapter 2: Situated cognition Chapter 3: Situated robotics Chapter 4: Situated approach (artificial intelligence) Chapter 5: Intelligent agent Chapter 6: Embodied cognition Chapter 7: Virtual intelligence Chapter 8: Smart object Chapter 9: Computer-supported collaborative learning Chapter 10: Technoself studies (II) Answering the public top questions about situated artificial intelligence. (III) Real world examples for the usage of situated artificial intelligence in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of situated artificial intelligence. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Naive Bayes Classifier In the field of statistics, naive Bayes classifiers are a family of straightforward "probabilistic classifiers" that are derived from the application of Bayes' theorem with strong (naive) assumptions of independence between the features. They are among the Bayesian network models that are the simplest, but when combined with kernel density estimation, they are capable of achieving great levels of accuracy. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Naive Bayes classifier Chapter 2: Likelihood function Chapter 3: Bayes' theorem Chapter 4: Bayesian inference Chapter 5: Multivariate normal distribution Chapter 6: Maximum likelihood estimation Chapter 7: Bayesian network Chapter 8: Naive Bayes spam filtering Chapter 9: Marginal likelihood Chapter 10: Dirichlet distribution (II) Answering the public top questions about naive bayes classifier. (III) Real world examples for the usage of naive bayes classifier in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of naive bayes classifier' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of naive bayes classifier.

What Is Neuroevolution Neuroevolution, sometimes spelled neuro-evolution, is a form of artificial intelligence that generates artificial neural networks (ANN), parameters, and rules through the application of evolutionary algorithms. Neuroevolution is also spelled neuro-evolution. The most popular applications for this technique are found in evolutionary robotics, artificial life, and general game playing. The primary advantage is that neuroevolution may be applied to a wider variety of problems than supervised learning methods, which need a curriculum of accurate input-output pairings to function properly. Neuroevolution, on the other hand, needs nothing more than a measurement of how well a network performs at a given job. For instance, the result of a game can be easily measured even if the necessary strategies are not provided in the form of labeled examples. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. It can be contrasted with conventional deep learning approaches, which make use of gradient descent on a neural network that possesses a fixed topology. Neuroevolution is frequently utilized as a component of the reinforcement learning paradigm. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Neuroevolution Chapter 2: Artificial neural network Chapter 3: Evolutionary algorithm Chapter 4: Genetic representation Chapter 5: Effective fitness Chapter 6: Neuroevolution of augmenting topologies Chapter 7: Recurrent neural network Chapter 8: Compositional pattern-producing network Chapter 9: HyperNEAT Chapter 10: Evolving intelligent system (II) Answering the public top questions about neuroevolution. (III) Real world examples for the usage of neuroevolution in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of neuroevolution. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Bio Inspired Computing The subject of research known as bio-inspired computing, which is short for biologically inspired computing, is one that focuses on finding solutions to issues in computer science by employing biological models. Connectionism, emergent behavior, and emergence are all related to this concept. Within the field of computer science, artificial intelligence and machine learning are related to the concept of bio-inspired computing. The field of natural computation includes a significant subfield known as bio-inspired computing. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Bio-inspired computing Chapter 2: Connectionism Chapter 3: Evolutionary computation Chapter 4: Computational neuroscience Chapter 5: Neuromorphic engineering Chapter 6: Cognitive architecture Chapter 7: Neural network Chapter 8: Artificial brain Chapter 9: Computational neurogenetic modeling Chapter 10: Spiking neural network (II) Answering the public top questions about bio inspired computing. (III) Real world examples for the usage of bio inspired computing in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of bio inspired computing. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Hybrid Neural Networks The phrase "hybrid neural network" can refer to either biological neural networks that interact with artificial neuronal models or artificial neural networks that also have a symbolic component. Both of these interpretations are possible. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Hybrid neural network Chapter 2: Connectionism Chapter 3: Computational neuroscience Chapter 4: Symbolic artificial intelligence Chapter 5: Neuromorphic engineering Chapter 6: Recurrent neural network Chapter 7: Neural network Chapter 8: Neuro-fuzzy Chapter 9: Spiking neural network Chapter 10: Hierarchical temporal memory (II) Answering the public top questions about hybrid neural networks. (III) Real world examples for the usage of hybrid neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of hybrid neural networks. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Attractor Networks A sort of recurrent dynamical network known as an attractor network is one that gradually settles into a consistent pattern over the course of time. The nodes that make up the attractor network gradually move in the direction of a pattern, which can be either fixed-point, cyclic, chaotic, or random (stochastic). In the field of computational neuroscience, attractor networks have been extensively utilized to mimic neural processes including associative memory and motor behavior. Additionally, these networks have been utilized in biologically inspired machine learning techniques.An attractor network is made up of a collection of n nodes, each of which can be interpreted as a vector in a space of d dimensions, with n being more than d. Over the course of time, the state of the network will eventually gravitate toward one of a set of predetermined states located on a d-manifold. These states are known as the attractors. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Attractor network Chapter 2: Artificial neural network Chapter 3: Hebbian theory Chapter 4: Hopfield network Chapter 5: Recurrent neural network Chapter 6: Autoassociative memory Chapter 7: Bidirectional associative memory Chapter 8: Competitive learning Chapter 9: Types of artificial neural networks Chapter 10: Dynamical neuroscience (II) Answering the public top questions about attractor networks. (III) Real world examples for the usage of attractor networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of attractor networks. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Control System Control loops are utilized in the management, commanding, directing, or regulation of the behavior of other devices or systems by a control system. It can range from something as simple as a single controller for a home heating system that uses a thermostat to operate a domestic boiler to something as complex as a big industrial control system that is used for controlling processes or machines. The control engineering design process is utilized to develop the control systems. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Control system Chapter 2: Control engineering Chapter 3: Control theory Chapter 4: Programmable logic controller Chapter 5: PID controller Chapter 6: Automation Chapter 7: Closed-loop controller Chapter 8: Open-loop controller Chapter 9: Industrial process control Chapter 10: Control loop (II) Answering the public top questions about control system. (III) Real world examples for the usage of control system in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of control system' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of control system.

What Is Subsumption Architecture The term "subsumption architecture" refers to a type of reactive robotic architecture that is closely linked to the field of behavior-based robotics, which enjoyed a great deal of success in the 1980s and 1990s. In 1986, Rodney Brooks and his colleagues were the ones who first coined the word. The concept of subsumption has had a significant impact on autonomous robots as well as other areas of real-time artificial intelligence. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Subsumption architecture Chapter 2: Behavior-based robotics Chapter 3: Rodney Brooks Chapter 4: Decentralised system Chapter 5: Model-based reasoning Chapter 6: Intelligent agent Chapter 7: Action selection Chapter 8: Nouvelle AI Chapter 9: Hierarchical control system Chapter 10: Genghis (robot) (II) Answering the public top questions about subsumption architecture. (III) Real world examples for the usage of subsumption architecture in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of subsumption architecture. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

What Is Embodied Cognitive Science The fields of linguistics, psychology, neuroscience, philosophy, computer science/artificial intelligence, and anthropology all contribute to the multidisciplinary field of cognitive science, which is the scientific study of the mind and the activities that occur within it. This research investigates the nature of cognition as well as its activities and functions. Cognitive scientists investigate intellect and behavior, with a particular emphasis on the ways in which the nervous system represents, processes, and transforms information. Language, perception, memory, attention, reasoning, and emotion are all aspects of the mind that cognitive scientists are interested in studying. In order to gain a better understanding of these aspects of the mind, cognitive scientists draw from a variety of other academic disciplines, including psychology, artificial intelligence, philosophy, neurology, and anthropology. The study that is typically performed in cognitive science covers a wide range of organizational levels, including learning and decision making, logic and planning, neural circuitry, and modular brain organization. One of the most important ideas in cognitive science is the idea that "thinking can be best understood in terms of representational structures in the mind and computational procedures that operate on those structures." How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Cognitive Science Chapter 2: Perception Chapter 3: Cognitive Model Chapter 4: Embodied Cognitive Science Chapter 5: Embodied Cognition Chapter 6: Situated Cognition Chapter 7: Distributed Cognition Chapter 8: Enactivism Chapter 9: Extended Mind Thesis Chapter 10: Predictive Coding (II) Answering the public top questions about embodied cognitive science. (III) Real world examples for the usage of embodied cognitive science in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of embodied cognitive science' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of embodied cognitive science.

What Is Artificial Immune Systems In the field of artificial intelligence, artificial immune systems (AIS) are a classification of rule-based, computationally intelligent machine learning systems that take their cues from the fundamentals and procedures of the immune system of vertebrates. When it comes to finding solutions to problems, algorithms are frequently based after the learning and memory capabilities of the immune system. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Artificial immune system Chapter 2: Immunology Chapter 3: Adaptive immune system Chapter 4: Computational immunology Chapter 5: Clonal selection algorithm Chapter 6: Immune network theory Chapter 7: Evolutionary computation Chapter 8: Bio-inspired computing Chapter 9: Glossary of artificial intelligence Chapter 10: Rule-based machine learning (II) Answering the public top questions about artificial immune systems. (III) Real world examples for the usage of artificial immune systems in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial immune systems' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial immune systems.

What Is Multi Agent System A multi-agent system is a type of computerized system that is made up of numerous intelligent agents that communicate with one another. It is conceivable for multi-agent systems to solve problems that a single agent or a monolithic system would have a difficult or impossible time resolving on their own. Methodical, functional, and procedural techniques, algorithmic search, and learning through reinforcement are all examples of possible types of intelligence. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Multi-agent system Chapter 2: Distributed artificial intelligence Chapter 3: Software agent Chapter 4: Intelligent agent Chapter 5: Agent-based model Chapter 6: Swarm intelligence Chapter 7: Swarm robotics Chapter 8: Consensus dynamics Chapter 9: Agent-based social simulation Chapter 10: Agent mining (II) Answering the public top questions about multi agent system. (III) Real world examples for the usage of multi agent system in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of multi agent system' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of multi agent system.

What Is Cognitive Architecture The term "cognitive architecture" can refer to both a theory about the structure of the human mind as well as a computational implementation of such a theory that is utilized in the fields of artificial intelligence (AI) and computational cognitive science. The term "cognitive architecture" can also be used interchangeably with "psychological architecture." The formalized models can be utilized both for the purpose of further refining an all-encompassing theory of cognition as well as for the development of a practical artificial intelligence program. The ACT-R and SOAR cognitive architectures are examples of successful cognitive architectures. Allen Newell is credited as being the one who first started the research on cognitive architectures as a software implementation of cognitive theories in the year 1990. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Cognitive Architecture Chapter 2: Cognitive Science Chapter 3: Connectionism Chapter 4: ACT-R Chapter 5: Unified Theories of Cognition Chapter 6: Computational Cognition Chapter 7: Computational Theory of Mind Chapter 8: CLARION (cognitive architecture) Chapter 9: LIDA (cognitive architecture) Chapter 10: Differentiable Neural Computer (II) Answering the public top questions about cognitive architecture. (III) Real world examples for the usage of cognitive architecture in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of cognitive architecture' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of cognitive architecture.

What Is Support Vector Machine In the field of machine learning, support vector machines are supervised learning models that examine data for classification and regression analysis. These models come with related learning algorithms. Vladimir Vapnik and his coworkers at AT&T Bell Laboratories were responsible for its creation. Because they are founded on statistical learning frameworks or the VC theory, which was developed by Vapnik and Chervonenkis (1974), support vector machines (SVMs) are among the most accurate prediction systems. A non-probabilistic binary linear classifier is what results when an SVM training algorithm is given a series of training examples, each of which is marked as belonging to one of two categories. The algorithm then develops a model that assigns subsequent examples to either one of the two categories or neither of them. The support vector machine (SVM) allocates training examples to points in space in such a way as to maximize the difference in size between the two categories. After that, new examples are mapped into that same space, and depending on which side of the gap they fall on, a prediction is made as to which category they belong to. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Support vector machine Chapter 2: Linear classifier Chapter 3: Perceptron Chapter 4: Projection (linear algebra) Chapter 5: Linear separability Chapter 6: Kernel method Chapter 7: Sequential minimal optimization Chapter 8: Least-squares support vector machine Chapter 9: Hinge loss Chapter 10: Polynomial kernel (II) Answering the public top questions about support vector machine. (III) Real world examples for the usage of support vector machine in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of support vector machine' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of support vector machine.

What Is Nouvelle Artificial Intelligence In the 1980s, Rodney Brooks, who at the time worked as part of the artificial intelligence laboratory at MIT, laid the groundwork for what is now known as nouvelle artificial intelligence (AI), a methodology for artificial intelligence. New AI is a departure from traditional AI in that its objective is to endow robots with intelligence levels comparable to that of insects. Instead than relying on the created worlds that symbolic AIs generally needed to have programmed into them, researchers believe that intelligence can arise naturally from simple behaviors as these intelligences interact with the "real world." How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Nouvelle AI Chapter 2: Artificial intelligence Chapter 3: Subsumption architecture Chapter 4: Cog (project) Chapter 5: Behavior-based robotics Chapter 6: Rodney Brooks Chapter 7: Neats and scruffies Chapter 8: Physical symbol system Chapter 9: Embodied cognitive science Chapter 10: Situated approach (artificial intelligence) (II) Answering the public top questions about nouvelle artificial intelligence. (III) Real world examples for the usage of nouvelle artificial intelligence in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of nouvelle artificial intelligence. What Is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.