Neural networks and fuzzy systems

Nowadays, many aspects of electrical and electronic engineering are essentially applications of DSP.

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

Discover the skill-sets required to implement various approaches to Machine Learning with PythonKey FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook DescriptionUnsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome.

Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulatorKey FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken.

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

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.

This book examines the fundamentals and technologies of Artificial Intelligence (AI) and describes their tools, challenges, and issues.

Learn how to use AI and blockchain to build decentralized intelligent applications (DIApps) that overcome real-world challengesKey FeaturesUnderstand the fundamental concepts for converging artificial intelligence and blockchainApply your learnings to build apps using machine learning with Ethereum, IPFS, and MoiBitGet well-versed with the AI-blockchain ecosystem to develop your own DIAppsBook DescriptionAI and blockchain are two emerging technologies catalyzing the pace of enterprise innovation.

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.

A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

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

A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable modelsKey FeaturesLearn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation?

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.

Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network modelsKey FeaturesImplement GAN architectures to generate images, text, audio, 3D models, and moreUnderstand how GANs work and become an active contributor in the open source communityLearn how to generate photo-realistic images based on text descriptionsBook DescriptionWith continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning.

Diabetes Mellitus (DM, commonly referred to as diabetes, is a metabolic disorder in which there are high blood sugar levels over a prolonged period.

Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars.

Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric, and DGLKey FeaturesMaster new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL)Explore GML frameworks and their main characteristicsLeverage LLMs for machine learning on graphs and learn about temporal learningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGraph Machine Learning, Second Edition builds on its predecessor's success, delivering the latest tools and techniques for this rapidly evolving field.

Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data.

Machine Dreaming and Consciousness is the first book to discuss the questions raised by the advent of machine dreaming.

No detailed description available for "e;Applied Deep Learning on Graphs"e;.

Text production has many applications.

Explore TensorFlow's capabilities to perform efficient deep learning on imagesKey FeaturesDiscover image processing for machine visionBuild an effective image classification system using the power of CNNsLeverage TensorFlow's capabilities to perform efficient deep learningBook DescriptionTensorFlow is Google's popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.

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.

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey FeaturesGet up and running with artificial intelligence in no time using hands-on problem-solving recipesExplore popular Python libraries and tools to build AI solutions for images, text, sounds, and imagesImplement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much moreBook DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving.

A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Amidst the rampant use of algorithmization enabled by AI, the common theme of AI systems is the human factor.

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

This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating machinery.

Our book, Future of Tech Startups and Innovations in the Age of AI, mainly focuses on artificial intelligence (AI) tools, AI-based startups, AI-enabled innovations, Autonomous AI Agents (Auto-GPT), AI-based marketing startups, machine learning for organizations, AI-internet of things (IoT) for new tech companies, AI-enabled drones for agriculture industry, machine learning (ML)/deep learning (DL)-based drip farming, AI-based driverless cars, AI-based weather prediction startups, AI tools for personal branding, AI-based teaching, AI-based doctor/hospital startups, AI for game companies, AI-based finance tools, AI for human resource management, AI-powered management tools, AI tools for future pandemics, AI/ML-based transportation companies, AI for media, AI for carrier counseling, AI for customer care, AI for next generation businesses, and many more applications.

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.

This book explores the application of the complex relationship between concept drift and cutting-edge large language models to address the problems and opportunities in navigating changing data landscapes.

"e;The authors' clear visual style provides a comprehensive look at what's currently possible with artificial neural networks as well as a glimpse of the magic that's to come.

A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit, including SHAP, feature importance, and causal inference, to build fairer, safer, and more reliable models.

Business Modeling and Data Mining demonstrates how real world business problems can be formulated so that data mining can answer them.

The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety.

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

Discover interesting recipes to help you understand the concepts of object detection, image processing, and facial detectionKey FeaturesExplore the latest features and APIs in OpenCV 4 and build computer vision algorithmsDevelop effective, robust, and fail-safe vision for your applicationsBuild computer vision algorithms with machine learning capabilitiesBook DescriptionOpenCV is an image and video processing library used for all types of image and video analysis.

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.

"e;The authors' clear visual style provides a comprehensive look at what's currently possible with artificial neural networks as well as a glimpse of the magic that's to come.

This book is a follow-up to the IChemE symposium on "e;Neural Networks and Other Learning Technologies"e;, held at Imperial College, UK, in May 1999.

Leverage the full potential of SAS to get unique, actionable insights from your dataKey FeaturesBuild enterprise-class data solutions using SAS and become well-versed in SAS programmingWork with different data structures, and run SQL queries to manipulate your dataExplore essential concepts and techniques with practical examples to confidently pass the SAS certification examBook DescriptionSAS is one of the leading enterprise tools in the world today when it comes to data management and analysis.

This book describes the uses of different mathematical modeling and soft computing techniques used in epidemiology for experiential research in projects such as how infectious diseases progress to show the likely outcome of an epidemic, and to contribute to public health interventions.

This book offers a comprehensive overview of the theory and practical applications of swarm intelligence in fog computing, beyond 5G networks, and information security.

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.

Visualize and build deep learning models with 3D data using PyTorch3D and other Python frameworks to conquer real-world application challenges with easeKey FeaturesUnderstand 3D data processing with rendering, PyTorch optimization, and heterogeneous batchingImplement differentiable rendering concepts with practical examplesDiscover how you can ease your work with the latest 3D deep learning techniques using PyTorch3DBook DescriptionWith this hands-on guide to 3D deep learning, developers working with 3D computer vision will be able to put their knowledge to work and get up and running in no time.

Multi-robot systems are a major research topic in robotics.

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.

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.

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.