Neural networks and fuzzy systems

The goal of neurobionics is to elaborate methods for the repairment and substitution of impaired functions of the human nervous system.

This book collects together refereed versions of papers presented at the Eighth Neural Computation and Psychology Workshop (NCPW 8).

This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.

With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activitiesKey FeaturesLearn about AI and ML algorithms from the perspective of a seasoned data scientistGet practical experience in ML algorithms, such as regression, tree algorithms, clustering, and moreDesign neural networks that emulate the human brainBook DescriptionYou already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine.

The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks.

This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism.

Revolutionizing Communication: The Role of Artificial Intelligence explores the wide-ranging effects of artificial intelligence (AI) on how we connect and communicate, changing social interactions, relationships, and the very structure of our society.

Revolutionary and original, this treatise presents a new paradigm of EMERGENCE and COMPLEXITY, with applications drawn from numerous disciplines, including artificial life, biology, chemistry, computation, physics, image processing, information science, etc.

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and KerasKey FeaturesExplore neural network architecture and understand how it functionsLearn algorithms to solve common problems using back propagation and perceptronsUnderstand how to apply neural networks to applications with the help of useful illustrationsBook DescriptionNeural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics.

This book is of interest to researchers in universities, research centres and industries who are involved in measurements and need advanced mathematical tools to solve their problems, and to whoever is working in the development of these mathematical tools.

Create end-to-end systems that can power robots with artificial vision and deep learning techniquesKey FeaturesStudy ROS, the main development framework for robotics, in detailLearn all about convolutional neural networks, recurrent neural networks, and roboticsCreate a chatbot to interact with the robotBook DescriptionArtificial Vision and Language Processing for Robotics begins by discussing the theory behind robots.

Companies are spending billions on machine learning projects, but its money wasted if the models cant be deployed effectively.

Master machine learning techniques with real-world projects that interface TensorFlow with R, H2O, MXNet, and other languagesKey FeaturesGain expertise in machine learning, deep learning and other techniquesBuild intelligent end-to-end projects for finance, social media, and a variety of domainsImplement multi-class classification, regression, and clusteringBook DescriptionR is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain.

Implement reinforcement learning techniques and algorithms with the help of real-world examples and recipesKey FeaturesUse PyTorch 1.

Explore the world of neural networks by building powerful deep learning models using the R ecosystemKey FeaturesGet to grips with the fundamentals of deep learning and neural networksUse R 3.

We create technology enabling us to do things never before possible and it ultimately changes the way we live, work, play, and interact with each other.

May 11, 1997, was a watershed moment in the history of artificial intelligence (AI): the IBM supercomputer chess engine, Deep Blue, beat the world Chess champion, Garry Kasparov.

Modelling Invasive Alien Plant Species: Fuzzy Based Uncertainty presents the application of different fuzzy set theory techniques in developing risk assessment models for invasive plant species- those whose introduction and spread outside their natural range threatens local biodiversity.

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.

Machine learning, Internet of Things (IoT) and data analytics are new and fresh technologies that are being increasingly adopted in the field of medicine.

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

This is an interdisciplinary book for knowledge workers in business, finance, management, and socio-economic sciences.

This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems associated with biometric security such as authentication, indexing, template protection, spoofing attack detection, ROI detection, gender classification etc.

As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modelling and optimization of food drying processes.

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.

The Pacific Symposium on Biocomputing (PSB 2004) is an international, multidisciplinary conference for the presentation and discussion of current research on the theory and application of computational methods in problems of biological significance.

Implement TensorFlow's offerings such as TensorBoard, TensorFlow.

Leverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library.

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.

Design clever algorithms that discover hidden patterns and draw responses from unstructured, unlabeled data.

This book provides an overview of Federated Learning and how it can be used to build real-world AI-enabled applications.

Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics.

This lecture note volume is mainly about the recent development that connected neural network modeling to the theoretical physics of disordered systems.

Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domainsKey FeaturesExplore graph data in real-world systems and leverage graph learning for impactful business resultsDive into popular and specialized deep neural architectures like graph convolutional and attention networksLearn how to build scalable and productionizable graph learning solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWith their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs).

Computational Social Psychology showcases a new approach to social psychology that enables theorists and researchers to specify social psychological processes in terms of formal rules that can be implemented and tested using the power of high speed computing technology and sophisticated software.

Artificial Intelligence (AI) is undoubtedly playing an increasingly significant role in automobile technology.

Applying artificial intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields.

Nanoscale devices attracted significant research effort from the industry and academia due to their operation principals being based on different physical properties which provide advantages in the design of certain classes of circuits over conventional CMOS transistors.

This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and ClusteringKey FeaturesYour handy guide to building machine learning workflows in Go for real-world scenariosBuild predictive models using the popular supervised and unsupervised machine learning techniquesLearn all about deployment strategies and take your ML application from prototype to production readyBook DescriptionMachine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology.

The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology.

Build and train scalable neural network models on various platforms by leveraging the power of Caffe2Key FeaturesMigrate models trained with other deep learning frameworks on Caffe2Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devicesLeverage the distributed capabilities of Caffe2 to build models that scale easilyBook DescriptionCaffe2 is a popular deep learning library used for fast and scalable training and inference of deep learning models on various platforms.

To be effective at a senior level, IT professionals need not only appropriate technical skills and experience, but also a broad understanding of the context in which they operate.

This book contains twenty-two original contributions that provide a comprehensive overview of computational approaches to understanding a single neuron structure.

Machine learning has undergone rapid growth in diversification and practicality, and the repertoire of techniques has evolved and expanded.

Understand the fundamentals and develop your own AI solutions in this updated edition packed with many new examplesKey FeaturesAI-based examples to guide you in designing and implementing machine intelligenceBuild machine intelligence from scratch using artificial intelligence examplesDevelop machine intelligence from scratch using real artificial intelligenceBook DescriptionAI has the potential to replicate humans in every field.

Build smarter applications with AI capabilities using Microsoft Cognitive Services APIs without much hassleKey FeaturesExplore the Cognitive Services APIs for building machine learning applicationsBuild applications with computer vision, speech recognition, and language processing capabilitiesLearn to implement human-like cognitive intelligence for your applicationsBook DescriptionMicrosoft Cognitive Services is a set of APIs for adding intelligence to your application and leverage the power of AI to solve any business problem using the cognitive capabilities.