Neural networks and fuzzy systems

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning.

This book provides a comprehensive analysis of the tools and techniques used today for designing and modeling of efficient and robust swarm-intelligence based systems: highly (or fully) decentralized, semi-autonomous, highly-scalable infrastructures in various real-life scenarios.

Since the invention of computers and other similar machines, scientists and researchers have been trying very hard to enhance their capabilities to perform various tasks.

In recent years Genetic Algorithms (GA) and Artificial Neural Networks (ANN) have progressively increased in importance amongst the techniques routinely used in chemometrics.

Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers.

This solid introduction uses the principles of physics and the tools of mathematics to approach fundamental questions of neuroscience.

This book aims to teach the core concepts that make Self-driving vehicles (SDVs) possible.

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guideKey FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook DescriptionVarious intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome.

This book provides theoretical and practical knowledge on AI and swarm intelligence.

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning.

Object Detection with Deep Learning Models discusses recent advances in object detection and recognition using deep learning methods, which have achieved great success in the field of computer vision and image processing.

Recognized as a "e;Recommended"e; title by Choice for their April 2021 issue.

Artificial Neural Network-based Optimized Design of Reinforced Concrete Structures introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice.

Discover how to master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficiently and effectivelyKey FeaturesBuild End-to-End NLP Workflows, From Local Development to Production with Weasel and FastAPIMaster No-Training NLP Development with spaCy-LLM, From Prompt Engineering to Custom TasksCreate Advanced NLP Solutions, From Custom Components to Neural Coreference ResolutionBook DescriptionMastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem.

Perform supervised and unsupervised machine learning and learn advanced techniques such as training neural networks.

Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolutionKey FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook DescriptionNeuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes.

Originally published in 1996 as a special issue journal, Artificial Intelligence Applications on Wall Street, presents a series of articles derived from papers at the Third International Conference on Artificial Intelligence Applications on Wall Street.

AI in and for Africa: A Humanistic Perspective explores the convoluted intersection of artificial intelligence (AI) with Africa's unique socio-economic realities.

What is artificial intelligence?

Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolutionKey FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook DescriptionNeuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes.

The book demystifies the concept of Artificial Intelligence (AI) in a friendly manner to kids, with the goal of stimulating their curiosity and driving their interest in learning about AI.

Not everything is black and white.

From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure.

From ecosystems to Facebook, from the Internet to the global financial market, some of the most important and familiar natural systems and social phenomena are based on a networked structure.

Not everything is black and white.

Summary Deep learning has transformed the fields of computer vision, image processing, and natural language applications.

AI as a Service is a practical handbook to building and implementing serverless AI applications, without bogging you down with a lot of theory.

Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data.

Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it).

Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems.

Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks.

Deep learning systems have gotten really great at identifying patterns in text, images, and video.

A hands-on guide to powerful graph-based deep learning models.