This book provides a starting point for software professionals to apply artificial neural networks for software reliability prediction without having analyst capability and expertise in various ANN architectures and their optimization.
Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial neural networks application in software reliability prediction during testing phase as well as early phases of software development process are presented. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators.
Автор: Ming Zhang Название: Emerging Capabilities and Applications of Artificial Higher Order Neural Networks ISBN: 1799835634 ISBN-13(EAN): 9781799835639 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 239310.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields.
Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.
Автор: Ming Zhang Название: Emerging Capabilities and Applications of Artificial Higher Order Neural Networks ISBN: 1799835642 ISBN-13(EAN): 9781799835646 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 181110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields.
Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.
Автор: Ansari Shamshad Название: Building Computer Vision Applications Using Artificial Neural Networks: With Step-By-Step Examples in Opencv and Tensorflow with Python ISBN: 148425886X ISBN-13(EAN): 9781484258866 Издательство: Springer Рейтинг: Цена: 30740.00 T Наличие на складе: Невозможна поставка. Описание: Section 11. Chapter 1: Prerequisite and Software Installation 1.1. Python and PIP 1.1.1. Installing Python and PIP on Ubuntu 1.1.2. Installing Python and PIP on Mac OS 1.1.3. Installing Python and PIP on CentOS 7 1.1.4. Installing Python and PIP on Windows 1.2. Virtualenv 1.2.1. Setup and activate virtualenv 1.3. Tensorflow 1.3.1. Installing Tensorflow 1.4. PyCharm IDE 1.4.1. Installing PyCharm 1.4.2. Configuring PyCharm to use virtualenv 1.5. OpenCV 1.5.1. Installing OpenCV 1.5.2. Installing OpenCV4 with Python bindings 1.6. Additional libraries 1.6.1. SciPy 1.6.2. Matplotlib Chapter 2: Core Concepts of Image and Video Processing 1.7. Image processing 1.7.1. Image basics 1.7.2. Pixel 1.7.3. Pixel color 1.7.3.1. Grayscale 1.7.3.2. Color 1.7.4. Coordinate system 1.7.5. Python and OpenCV code to manipulate images 1.7.6. Program: loading, exploring and showing image 1.7.7. Program: OpenCV code to access and manipulate pixels 1.8. Drawing 1.8.1. Drawing a line on an image 1.8.2. Drawing a rectangle on an image 1.8.3. Drawing a circle on an image 1.9. Chapter summary 1.10. 2. Chapter 3: Techniques of Image Processing 2.1. Transformation 2.1.1. Resizing 2.1.2. Translation 2.1.3. Rotation 2.1.4. Flipping 2.1.5. Cropping 2.2. Image arithmetic and bitwise operations 2.2.1. Addition 2.2.2. Subtraction 2.2.3. Bitwise operations 2.2.3.1. OR 2.2.3.2. AND 2.2.3.3. NOT 2.2.3.4. XOR 2.3. Masking 2.4. Splitting and merging channels 2.5. Smoothing and blurring 2.6. Thresholding 2.7. Gradient and edge detection 2.8. Contours2.9. Chapter summary Section 23. Chapter 4: Building Artificial Intelligence System For Computer Vision 3.1. Image processing pipeline 3.2. Feature extraction 3.2.1. Color histogram 3.2.2. GLCM 3.2.3. HOG 3.2.4. LBP 3.3. Feature selection 3.3.1. Filter 3.3.2. Wrapper 3.3.3. Embedded 3.3.4. Regularization 3.4. Chapter summary 4. Chapter 5: Artificial Neural Network for Computer Vision 4.1. Introduction to ANN 4.1.1. ANN topology 4.1.2. Hyperparameters 4.1.3. ANN model training using TensorFlow 4.1.4. Model evaluation 4.1.5. Model deployment 4.1.6. Use of trained model 4.2. Introduction to Convolution Neural Network (CNN)4.2.1. Core concepts of CNN4.2.2. Creating training set for CNN4.2.3. Training CNN model using TensorFlow 4.2.4. Inspecting CNN model and evaluating model fitness4.2.5. Using and deployment of trained model4.3. Introduction to Recurrent Neural Network (RNN) and long short-term Memory (LSTM)4.3.1. Core concepts of RNN and LSTM4.3.2. Creating training set for LSTM4.3.3. LSTM model training using TensorFlow4.3.4. Inspecting LSTM model and assessing fitness4.3.5. Deploying LSTM models in practice Section 35. Chapter 6: Practical Example 1- Object Detection in Images 6. Chapter 7: Practical Example 2- Object Tracking in Videos 7. Chapter 8: Practical Example 3- Facial Detection 8. Chapter 9: Industrial Application - Realtime Defect Detection in Industrial Manufacturing
Автор: Sathiyamoorthi Velayutham Название: Handbook of Research on Applications and Implementations of Machine Learning Techniques ISBN: 1522599029 ISBN-13(EAN): 9781522599029 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 264270.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.
The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
Автор: Alanis, Alma Название: Artificial Neural Networks for Engineering Applications ISBN: 0128182474 ISBN-13(EAN): 9780128182475 Издательство: Elsevier Science Рейтинг: Цена: 114530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Hoe bestuur je een wendbare organisatie, of beter, hoe bestuur je een organisatie naar een blijvende wendbare organisatie?¢ Ben jij lid van het Managementteam (MT) of lid van de directie die de noodzaak tot verandering in besturing ziet, die de urgentie voelt om daar iets aan te doen en gehoor hiervoor wil vinden bij de collega leden van het MT of directie? ¢ Ben jij een coach in een organisatie die beweging richting een wendbare organisatie vooral bottom up ziet groeien, een beweging waar je de top down beweging aan toe wil voegen?. In deze pocketguide vind je een praktische methode hoe dit aan te pakken. Besturen in een steeds sneller veranderende wereld. Met de waan van de dag die vaak veel aandacht vraagt en die je kan afleiden van de te behalen resultaten. De auteurs gaan in op het operationaliseren van de strategische organisatiedoelen en daarmee het besturen van de gehele organisatie. De stellingname van dit boek is: maak scherp wat dit kwartaal bereikt moet worden om de strategische doelen te bereiken. Stuur kort cyclisch om te kunnen reageren op veranderende klantwensen of gewijzigde wet- en regelgeving. Werk samen als managementteam of directie richting dje strategische doelen en voorkom dat iedereen in de organisatie vooral een eigen doel nastreeft. Breng meer focus in de operationalisering van de strategie, minder met "brandjes" bezig zijn en meer met het voorkomen ervan. Krijg snel helder wat je medewerkers belemmert in hun werk. Lukt het om de belemmeringen in jouw organisatie snel op te lossen? De kern van deze pocketguide betreft het FOCUS- bord. Deze manier van visual management is een krachtig middel in de besturing. De toepassing ervan zorgt voor samenwerking tussen alle lagen in de organisatie, kort cyclisch sturen en focus op het behalen van de strategische doelen.
Автор: Hamilton William L. Название: Graph Representation Learning ISBN: 1681739631 ISBN-13(EAN): 9781681739632 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 57290.00 T Наличие на складе: Невозможна поставка. Описание: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing subset of graph representation learning.
Автор: Kwok Tai Chui, Miltiadis D. Lytras, Ryan Wen Liu, Mingbo Zhao Название: Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence ISBN: 1799830381 ISBN-13(EAN): 9781799830382 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 210670.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: While cognitive informatics and natural intelligence are receiving greater attention by researchers, multidisciplinary approaches still struggle with fundamental problems involving psychology and neurobiological processes of the brain. Examining the difficulties of certain approaches using the tools already available is vital for propelling knowledge forward and making further strides.
Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence is a collection of innovative research that examines the enhancement of human cognitive performance using emerging technologies. Featuring research on topics such as parallel computing, neuroscience, and signal processing, this book is ideally designed for engineers, computer scientists, programmers, academicians, researchers, and students.
Автор: Kwok Tai Chui, Miltiadis D. Lytras, Ryan Wen Liu, Mingbo Zhao Название: Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence ISBN: 179983039X ISBN-13(EAN): 9781799830399 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 291210.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: While cognitive informatics and natural intelligence are receiving greater attention by researchers, multidisciplinary approaches still struggle with fundamental problems involving psychology and neurobiological processes of the brain. Examining the difficulties of certain approaches using the tools already available is vital for propelling knowledge forward and making further strides.
Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence is a collection of innovative research that examines the enhancement of human cognitive performance using emerging technologies. Featuring research on topics such as parallel computing, neuroscience, and signal processing, this book is ideally designed for engineers, computer scientists, programmers, academicians, researchers, and students.
Автор: Karthikrajan Senthilnathan, Balamurugan Shanmugam, Dinesh Goyal, Iyswarya Annapoorani, Ravi Samikannu Название: Deep Learning Applications and Intelligent Decision Making in Engineering ISBN: 1799821099 ISBN-13(EAN): 9781799821090 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 166320.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is designed for engineers, computer scientists, programmers, software engineers, researchers, academics, and students.
Автор: Karthikrajan Senthilnathan, Balamurugan Shanmugam, Dinesh Goyal, Iyswarya Annapoorani, Ravi Samikannu Название: Deep Learning Applications and Intelligent Decision Making in Engineering ISBN: 1799821080 ISBN-13(EAN): 9781799821083 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 219910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process.
Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.
Get a head start in the world of AI and deep learning by developing your skills with PyTorch
Key Features
Learn how to define your own network architecture in deep learning
Implement helpful methods to create and train a model using PyTorch syntax
Discover how intelligent applications using features like image recognition and speech recognition really process your data
Book Description
Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch.
It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures.
The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.
By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.
What you will learn
Explore the different applications of deep learning
Understand the PyTorch approach to building neural networks
Create and train your very own perceptron using PyTorch
Solve regression problems using artificial neural networks (ANNs)
Handle computer vision problems with convolutional neural networks (CNNs)
Perform language translation tasks using recurrent neural networks (RNNs)
Who this book is for
This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.
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