Neural networks and fuzzy systems

Businesses today are faced with a highly competitive market and fast-changing technologies.

Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing.

Rapid advancements in mobile computing and communication technology and recent technological progress have opened up a plethora of opportunities.

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.

Leverage the power of reward-based training for your deep learning models with PythonKey FeaturesUnderstand Q-learning algorithms to train neural networks using Markov Decision Process (MDP)Study practical deep reinforcement learning using Q-NetworksExplore state-based unsupervised learning for machine learning modelsBook DescriptionQ-learning is a machine learning algorithm used to solve optimization problems in artificial intelligence (AI).

This book treats visual feedback control of mechanical systems, mostly robot manipulators.

Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents.

Methods based on neural networks are assuming an increasing role in biomedical research.

Bring magic to your mobile apps using TensorFlow Lite and Core MLKey FeaturesExplore machine learning using classification, analytics, and detection tasks.

Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks.

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks.

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques.

Implement neural network architectures by building them from scratch for multiple real-world applications.

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data.

A practical guide to mastering reinforcement learning algorithms using KerasKey FeaturesBuild projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into actionGet to grips with Keras and practice on real-world unstructured datasetsUncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learningBook DescriptionReinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks.

Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratchKey FeaturesInstall and configure Deeplearning4j to implement deep learning models from scratchExplore recipes for developing, training, and fine-tuning your neural network models in JavaModel neural networks using datasets containing images, text, and time-series dataBook DescriptionJava is one of the most widely used programming languages in the world.

The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously.

There are many myths and mistakes which make the topics of artificial intelligence complex and confusing.

Deep learning has achieved impressive results in image classification, computer vision, and natural language processing.

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.

Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology.

Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with PythonKey FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook DescriptionRemodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI).

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework.

The revitalization of neural network research in the past few years has already had a great impact on research and development in pattern recognition and artificial intelligence.

This book explores the intuitive appeal of neural networks and the genetic algorithm in finance.

Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities.

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing.

Cloud computing offers subscription-based on-demand services, and it has emerged as the backbone of the computing industry.

Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questionsKey FeaturesImplement solutions to 50 real-world computer vision applications using PyTorchUnderstand the theory and working mechanisms of neural network architectures and their implementationDiscover best practices using a custom library created especially for this bookBook DescriptionDeep learning is the driving force behind many recent advances in various computer vision (CV) applications.

This volume contains the proceedings of the seventh Italian Workshop on Neural Nets WIRN VIETRI '95, organized by the International Institute for Advanced Scientific Studies 'E R Caianiello' (IIASS) and Societa Italiana Reti Neuroniche (SIREN).

This book introduces the reader to Artificial Intelligence and its importance to our future.

This unique compendium presents a comprehensive and self-contained theory of material development under imperfect information and its applications.

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithmsKey FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook DescriptionWith significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit.

This book gives a complete overview of cloud computing: its importance, its trends, innovations, and its amalgamation with other technologies.

Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover.

This book aims to describe in simple terms the new area of statistical mechanics known as spin-glasses, encompassing systems in which quenched disorder is the dominant factor.

Interactive media are a human-machine interface that allows people to connect with each other by making them active participants in the media they consume through text, graphics, audio and video.

Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science mainly focuses on the techniques of artificial intelligence (AI), Internet of Things (IoT) and data science for future communications systems.

Towards Neuromorphic Machine Intelligence: Spike-Based Representation, Learning, and Applications provides readers with in-depth understanding of Spiking Neural Networks (SNNs), which is a burgeoning research branch of Artificial Neural Networks (ANNs), AI, and Machine Learning that sits at the heart of the integration between Computer Science and Neural Engineering.

Theoretical Mechanics of Biological Neural Networks presents an extensive and coherent discusson and formulation of the generation and integration of neuroelectric signals in single neurons.

Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms.

Take a comprehensive and step-by-step approach to understanding machine learningKey FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook DescriptionMachine learning algorithms are an integral part of almost all modern applications.

Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an "e;on-and-off"e; fashion.

Fuzzy logic principles, practices, and real-world applicationsThis hands-on guide offers clear explanations of fuzzy logic along with practical applications and real-world examples.

Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learnKey FeaturesExploit the power of Python to explore the world of data mining and data analyticsDiscover machine learning algorithms to solve complex challenges faced by data scientists todayUse Python libraries such as TensorFlow and Keras to create smart cognitive actions for your projectsBook DescriptionThe surge in interest in machine learning (ML) is due to the fact that it revolutionizes automation by learning patterns in data and using them to make predictions and decisions.

Uncertainty is certain to be found in structural engineering, making it crucial to structure design.