Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, Segall Richard S., Niu Gao
Автор: Zhang Название: Toward Deep Neural Networks ISBN: 1138387037 ISBN-13(EAN): 9781138387034 Издательство: Taylor&Francis Рейтинг: Цена: 127600.00 T Наличие на складе: Нет в наличии. Описание: This book introduces deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors` 20 years of research experience on neuronets, the book explores the models, algorithms, and applications of the WASD neuronet.
Автор: Bert Kappen; Stan Gielen Название: Neural Networks: Artificial Intelligence and Industrial Applications ISBN: 3540199926 ISBN-13(EAN): 9783540199922 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This volume contains papers presented at the Third Annual SNN Symposium on Neural Networks held in Nijmegen, 1995. It summarizes developments in neurobiology, the cognitive sciences, robotics, and vision and data modelling. Working neural network solutions to industrial problems are also presented.
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.
Автор: Quang Hung Do Название: Artificial Neural Network Applications in Business and Engineering ISBN: 1799832384 ISBN-13(EAN): 9781799832386 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 260570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In today's modernized market, various disciplines continue to search for universally functional technologies that improve upon traditional processes. Artificial neural networks are a set of statistical modeling tools that are capable of processing nonlinear data with strong accuracy. Due to their complexity, utilizing their potential was previously seen as a challenge. However, with the development of artificial intelligence, this technology has proven to be an effective and efficient problem-solving method.
Artificial Neural Network Applications in Business and Engineering is an essential reference source that illustrates recent advancements of artificial neural networks in various professional fields, accompanied by specific case studies and practical examples. Featuring research on topics such as training algorithms, transportation, and computer security, this book is ideally designed for researchers, students, developers, managers, engineers, academicians, industrialists, policymakers, and educators seeking coverage on modern trends in artificial neural networks and their real-world implementations.
Название: Biomedical Applications of Nanoparticles/Grumezescu, Alexandru Mihai. William Andrew, 2019 9780128165065 ISBN: 0128165065 ISBN-13(EAN): 9780128165065 Издательство: Elsevier Science Рейтинг: Цена: 211100.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Biomedical Applications of Nanoparticles describes the most interesting and investigated biomedical applications of nanoparticles, emphasizing their therapeutic impact. Progress made in the therapy of severe diseases, such as cancer and difficult infections is strictly correlated to the scientific progress and technological development in the field of materials science. Nanoparticles have numerous therapeutic applications, starting with the design of new drugs, delivery systems, therapeutic materials, and their contribution to the development of preventive strategies. The book highlights the impact of nanoparticles on the therapy of infections, antimicrobial effect and also anti-cancer strategies. . Successful examples are given throughout the book, along with analysis in order to improve future outcomes of novel therapies.
Communication and network technology has witnessed recent rapid development and numerous information services and applications have been developed globally. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication. Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
Автор: Goldberg Yoav Название: Neural Network Methods in Natural Language Processing ISBN: 1627052984 ISBN-13(EAN): 9781627052986 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 76690.00 T Наличие на складе: Нет в наличии. Описание: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.
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.
Название: Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications ISBN: 1799804143 ISBN-13(EAN): 9781799804147 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 2494800.00 T Наличие на складе: Нет в наличии. Описание: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
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: 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: Saifullah Khalid Название: Applications of Artificial Intelligence in Electrical Engineering ISBN: 1799827186 ISBN-13(EAN): 9781799827184 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 283010.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies.
Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students.
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