wang1062807258的博客

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. E

Wheeled Mobile Robotics: From Fundamentals Towards Autonomous Systemscovers the main topics from the wide area of mobile robotics, explaining all applied theory and application. The book gives the reader a good foundation, enabling them to continue to more advanced topics. Several examples are included for better understanding, many of them accompanied by short MATLAB® script code making it easy to reuse in practical work. The book includes several examples of discussed methods and projects for wheeled mobile robots and some advanced methods for their control and localization. It is an ideal resource for those seeking an understanding of robotics, mechanics, and control, and for engineers and researchers in industrial and other specialized research institutions in the field of wheeled mobile robotics. Beginners with basic math knowledge will benefit from the examples, and engineers with an understanding of basic system theory and control will find it easy to follow the more demanding fundamental parts and advanced methods explained.

This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

Gain experience of building a next-generation collaboration robot Key Features • Get up and running with the fundamentals of robotic programming • Program a robot using Python and the Raspberry Pi 3 • Learn to build a smart robot with interactive and AI-enabled behaviors Book Description We live in an age where the most difficult human tasks are now automated. Smart and intelligent robots, which will perform different tasks precisely and efficiently, are the requirement of the hour. A combination of Raspberry Pi and Python works perfectly when making these kinds of robots. Learn Robotics Programming starts by introducing you to the basic structure of a robot, along with how to plan, build, and program it. As you make your way through the book, you will gradually progress to adding different outputs and sensors, learning new building skills, and writing code for interesting behaviors with sensors. You'll also be able to update your robot, and set up web, phone, and Wi-Fi connectivity in order to control it. By the end of the book, you will have built a clever robot that can perform basic artificial intelligence (AI) operations. What you will learn • Configure a Raspberry Pi for use in a robot • Interface motors and sensors with a Raspberry Pi • Implement code to make interesting and intelligent robot behaviors • Understand the first steps in AI behavior such as speech recognition visual processing • Control AI robots using Wi-Fi • Plan the budget for requirements of robots while choosing parts Who this book is for Learn Robotics Programming is for programmers, developers, and enthusiasts interested in robotics and developing a fully functional robot. No major experience required just some programming knowledge would be sufficient.

Learning about robotics will become an essential skill as it will be a ubiquitous part of life. Even though robotics is a complex subject, several other tools along with Python can help you design a project to create an easy-to-use interface. The main aim of the book is to teach you how to build an autonomous mobile robot from scratch.

Mobile robots have been increasingly applied in many different scenarios, such as space exploration and search and rescue, where the robots are required to travel over uneven terrain while outdoors. This book provides a new framework and the related algorithms for designing autonomous mobile robotic systems in such unknown outdoor environments.

Alexander Schaub examines how a reactive instinctive behavior, similar to instinctive reactions as incorporated by living beings, can be achieved for intelligent mobile robots to extend the classic reasoning approaches. He identifies possible applications for reactive approaches, as they enable a fast response time, increase robustness and have a high abstraction ability, even though reactive methods are not universally applicable. The chosen applications are obstacle avoidance and relative positioning ? which can also be utilized for navigation ? and a combination of both. The implementation of reactive instinctive behaviors for the identified tasks is then validated in simulation together with real world experiments. Contents Why Mobile Robots Should Have an Artificial Instinct Vision-Based Reactive Controllers Evaluation in Real World Tests and in Simulation Target Groups Lecturers and students in the field of Robotics, Control Engineering, Computer Vision Practitioners in the field of Intelligent Vehicles, Mobile Robots About the Author Alexander Schaub joined the Robotics and Mechatronics Center of the German Aerospace Center (DLR) in 2009 and worked in the development of a robotic electric vehicle and researched in the field of vision-based control and reactive instinctive behaviors for autonomous vehicle. In 2017, he started an MBA at HEC Paris and will continue working in the field of autonomous driving afterwards. Read more...

This book covers the start-of-the-art research and development for the emerging area of autonomous and intelligent systems. In particular, the authors emphasize design and validation methodologies to address the grand challenges related to safety. This book offers a holistic view of a broad range of technical aspects (including perception, localization and navigation, motion control, etc.) and application domains (including automobile, aerospace, etc.), presents major challenges and discusses possible solutions.

This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation. The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.

If a camera takes a picture and no one notices, did it really happen? In this book, we’re going to show you how to make a working Raspberry Pi–based camera system so that you can capture time-lapse images and view via WiFi, trigger the camera if motion is detected, and even carry out basic facial recognition as an introduction to machine learning methods. Take a Raspberry Pi and add a camera module, and you have a programmable camera. Add some software, and you can start to do interesting surveillance and automatic object recognition work with it. Activate the Pi as a WiFi node and you can do all these wonderful things from a distance. A good surveillance system does more than take pictures. It should also turn those pictures into actionable information that increases your knowledge. That’s now easily done in software, and we’re going to show you how. This book pulls together a set of little tricks—setting up Pi cameras, making a Pi broadcast as a WiFi device, adding time lapse and motion detection and face recognition, and sticking a battery pack on it so it can function anywhere—to create portable spy cameras. We’ve used these rigs in everything from “Find the Pi” party contests, to implementing privacy-respecting security in our lab, to showing off modern tech like facial recognition. Other uses could include monitoring deer and wildlife, checking your house mailbox for mail arrival, and capturing time-lapse sequences of natural events or traffic patterns.

Computer vision is one of the most widely studied sub-fields of computer science. It has several important applications, such as face detection, image searching, and artistic image conversion. With the popularity of deep learning methods, many recent applications of computer vision are in self-driving cars, robotics, medicine, Virtual reality, and Augmented reality. In this book, a practical approach of learning computer vision is shown. Using code blocks as well as a theoretical understanding of algorithms will help in building stronger computer vision fundamentals. This book teaches you how to create applications using standard tools such as OpenCV, Keras, and TensorFlow. The various concepts and implementations explained in this book can be used across several domains, such as robotics, image editing apps, and self-driving cars. In this book, each chapter is explained with accompanying code and results to enforce the learning together.

Hands-On Computer Vision with Julia is a thorough guide for developers who want to get started with building computer vision applications using Julia. Julia is well suited to image processing because it's easy to use and lets you write easy-to-compile and efficient machine code. This book begins by introducing you to Julia's image processing libraries such as Images.jl and ImageCore.jl. You'll get to grips with analyzing and transforming images using JuliaImages; some of the techniques discussed include enhancing and adjusting images. As you make your way through the chapters, you'll learn how to classify images, cluster them, and apply neural networks to solve computer vision problems. In the concluding chapters, you will explore OpenCV applications to perform real-time computer vision analysis, for example, face detection and object tracking. You will also understand Julia's interaction with Tesseract to perform optical character recognition and build an application that brings together all the techniques we introduced previously to consolidate the concepts learned. By end of the book, you will have understood how to utilize various Julia packages and a few open source libraries such as Tesseract and OpenCV to solve computer vision problems with ease. What you will learn Analyze image metadata and identify critical data using JuliaImages Apply filters and improve image quality and color schemes Extract 2D features for image comparison using JuliaFeatures Cluster and classify images with KNN/SVM machine learning algorithms Recognize text in an image using the Tesseract library Use OpenCV to recognize specific objects or faces in images and videos Build neural network and classify images with MXNet Who This Book Is For Hands-On Computer Vision with Julia is for Julia developers who are interested in learning how to perform image processing and want to explore the field of computer vision. Basic knowledge of Julia will help you understand the concepts more effectively.

This unique text/reference presents a fresh look at nonlinear processing through nonlinear eigenvalue analysis, highlighting how one-homogeneous convex functionals can induce nonlinear operators that can be analyzed within an eigenvalue framework. The text opens with an introduction to the mathemati

The field of mechatronics (which is the synergistic combination of precision mechanical engineering, electronic control and systems thinking in the design of products and manufacturing processes) is gaining much attention in industries and academics. It was detected that the topics of computer vision, control and robotics are imperative for the successful of mechatronics systems. This book includes several chapters which report successful study cases about computer vision, control and robotics. The readers will have the latest information related to mechatronics, that contains the details of implementation, and the description of the test scenarios.

Introduction to Visual Computing: Core Concepts in Computer Vision, Graphics, and Image Processing covers the fundamental concepts of visual computing. Whereas past books have treated these concepts within the context of specific fields such as computer graphics, computer vision or image processing, this book offers a unified view of these core concepts, thereby providing a unified treatment of computational and mathematical methods for creating, capturing, analyzing and manipulating visual data (e.g. 2D images, 3D models). Fundamentals covered in the book include convolution, Fourier transform, filters, geometric transformations, epipolar geometry, 3D reconstruction, color and the image synthesis pipeline. The book is organized in four parts. The first part provides an exposure to different kinds of visual data (e.g. 2D images, videos and 3D geometry) and the core mathematical techniques that are required for their processing (e.g. interpolation and linear regression.) The second part of the book on Image Based Visual Computing deals with several fundamental techniques to process 2D images (e.g. convolution, spectral analysis and feature detection) and corresponds to the low level retinal image processing that happens in the eye in the human visual system pathway. The next part of the book on Geometric Visual Computing deals with the fundamental techniques used to combine the geometric information from multiple eyes creating a 3D interpretation of the object and world around us (e.g. transformations, projective and epipolar geometry, and 3D reconstruction). This corresponds to the higher level processing that happens in the brain combining information from both the eyes thereby helping us to navigate through the 3D world around us. The last two parts of the book cover Radiometric Visual Computing and Visual Content Synthesis. These parts focus on the fundamental techniques for processing information arising from the interaction of light with objects around us, as well as the fundamentals of creating virtual computer generated worlds that mimic all the processing presented in the prior sections. The book is written for a 16 week long semester course and can be used for both undergraduate and graduate teaching, as well as a reference for professionals.

The research book is a continuation of the authors’ previous works, which are focused on recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms. The book gathers selected contributions addressing a number of real-life applications including the identification of handwritten texts, watermarking techniques, simultaneous localization and mapping for mobile robots, motion control systems for mobile robots, analysis of indoor human activity, facial image quality assessment, android device controlling, processing medical images, clinical decision-making and foot progression angle detection. Given the tremendous interest among researchers in the development and applications of computer vision paradigms in the field of business, engineering, medicine, security and aviation, the book offers a timely guide for all PhD students, professors, researchers and software developers working in the areas of digital video processing and computer vision technologies.

The research book is a continuation of the authors’ previous works, which are focused on recent advances in computer vision methodologies and technical solutions using conventional and intelligent paradigms. The book gathers selected contributions addressing aerial and satellite image processing and related fields. Topics covered include novel tensor and wave models, a new comparative morphology scheme, warping compensation in video stabilization, image deblurring based on physical processes of blur impacts, and a rapid and robust core structural verification algorithm for feature extraction in images and videos, among others. All chapters focus on practical implementations. Given the tremendous interest among researchers in the development and applications of computer vision paradigms in the field of business, engineering, medicine, security and aviation, this book offers a timely guide.

Artificial Intelligence (AI) is penetrating in all sciences as a multidisciplinary approach. However, adopting the theory of AI including computer vision and computer audition to urban intellectual space, is always difficult for architecture and urban planners. This book overcomes this challenge through a conceptual framework by merging computer vision and audition to urban studies based on a series of workshops called Remorph, conducted by Tehran Urban Innovation Center (TUIC).

This book presents a collection of high-quality research by leading experts in computer vision and its applications. Each of the 16 chapters can be read independently and discusses the principles of a specific topic, reviews up-to-date techniques, presents outcomes, and highlights the challenges and future directions. As such the book explores the latest trends in fashion creative processes, facial features detection, visual odometry, transfer learning, face recognition, feature description, plankton and scene classification, video face alignment, video searching, and object segmentation. It is intended for postgraduate students, researchers, scholars and developers who are interested in computer vision and connected research disciplines, and is also suitable for senior undergraduate students who are taking advanced courses in related topics. However, it is also provides a valuable reference resource for practitioners from industry who want to keep abreast of recent developments in this dynamic, exciting and profitable research field.

The goal of the Volume I Geometric Algebra for Computer Vision, Graphics and Neural Computing is to present a unified mathematical treatment of diverse problems in the general domain of artificial intelligence and associated fields using Clifford, or geometric, algebra. Geometric algebra provides a rich and general mathematical framework for Geometric Cybernetics in order to develop solutions, concepts and computer algorithms without losing geometric insight of the problem in question. Current mathematical subjects can be treated in an unified manner without abandoning the mathematical system of geometric algebra for instance: multilinear algebra, projective and affine geometry, calculus on manifolds, Riemann geometry, the representation of Lie algebras and Lie groups using bivector algebras and conformal geometry. By treating a wide spectrum of problems in a common language, this Volume I offers both new insights and new solutions that should be useful to scientists, and engineers working in different areas related with the development and building of intelligent machines. Each chapter is written in accessible terms accompanied by numerous examples, figures and a complementary appendix on Clifford algebras, all to clarify the theory and the crucial aspects of the application of geometric algebra to problems in graphics engineering, image processing, pattern recognition, computer vision, machine learning, neural computing and cognitive systems.

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Workshop on Graphics Recognition, GREC 2017, held in Kyoto, Japan, in November 2017. The 10 revised full papers presented were carefully reviewed and selected from 14 initial submissions. They contain both classical and emerging topics of graphics rcognition, namely analysis and detection of diagrams, search and classification, optical music recognition, interpretation of engineering drawings and maps.

This book constitutes the thoroughly refereed proceedings of the 15th International Conference on Image Analysis and Recognition, ICIAR 2018, held in Póvoa de Varzim, Portugal, in June 2018. The 91 full papers presented together with 15 short papers were carefully reviewed and selected from 179 submissions. The papers are organized in the following topical sections: Enhancement, Restoration and Reconstruction, Image Segmentation, Detection, Classication and Recognition, Indexing and Retrieval, Computer Vision, Activity Recognition, Traffic and Surveillance, Applications, Biomedical Image Analysis, Diagnosis and Screening of Ophthalmic Diseases, and Challenge on Breast Cancer Histology Images.

This book constitutes the refereed proceedings of the 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018, held in Havana, Cuba, in September 2018. The 42 full papers presented were carefully reviewed and selected from 101 submissions. The papers promote and disseminate ongoing research on mathematical methods and computing techniques for artificial intelligence and pattern recognition, in particular in bioinformatics, cognitive and humanoid vision, computer vision, image analysis and intelligent data analysis, as well as their application in a number of diverse areas such as industry, health, robotics, data mining, opinion mining and sentiment analysis, telecommunications, document analysis, and natural language processing and recognition.

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.

This book constitutes the thoroughly refereed post-conference proceedings of the 8th Pacific Rim Symposium on Image and Video Technology, PSIVT 2017, held in Wuhan, China, in November 2017. The total of 39 revised papers was carefully reviewed and selected from 91 submissions. The Pacific-Rim Symposium on Image and Video Technology (PSIVT) is a high-quality series of symposia that aim at providing a forum for researchers and practitioners who are being involved, or are contributing to theoretical advances or practical implementations in image and video technology.

This book constitutes revised selected papers from the Third International MICCAI Brainlesion Workshop, BrainLes 2017, as well as the International Multimodal Brain Tumor Segmentation, BraTS, and White Matter Hyperintensities, WMH, segmentation challenges, which were held jointly at the Medical Image computing for Computer Assisted Intervention Conference, MICCAI, in Quebec City, Canada, in September 2017. The 40 papers presented in this volume were carefully reviewed and selected from 46 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; and ischemic stroke lesion image segmentation.

This book constitutes the refereed post-conference proceedings of the 22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017, held in Valparaíso, Chile, in November 2017. The 87 papers presented were carefully reviewed and selected from 156 submissions. The papers feature research results in the areas of pattern recognition, image processing, computer vision, multimedia and related fields.

The book covers a comprehensive overview of the theory, methods, applications and tools of cognition and recognition. The book is a collection of best selected papers presented in the International Conference on Cognition and Recognition 2016 (ICCR 2016) and helpful for scientists and researchers in the field of image processing, pattern recognition and computer vision for advance studies. Nowadays, researchers are working in interdisciplinary areas and the proceedings of ICCR 2016 plays a major role to accumulate those significant works at one place. The chapters included in the proceedings inculcates both theoretical as well as practical aspects of different areas like nature inspired algorithms, fuzzy systems, data mining, signal processing, image processing, text processing, wireless sensor networks, network security and cellular automata.

The problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a system that automatically analyzes the structures of a music file and annotates the file with the perceived emotions. Further, it explores emotion detection in MIDI and audio files. In the experiments presented here, the categorical and dimensional approaches were used, and the knowledge and expertise of music experts with a university music education were used for music file annotation. The automatic emotion detection systems constructed and described in the book make it possible to index and subsequently search through music databases according to emotion. In turn, the emotion maps of musical compositions provide valuable new insights into the distribution of emotions in music and can be used to compare that distribution in different compositions, or to conduct emotional comparisons of different interpretations of the same composition.

This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications. Some

This book focuses on light invariant bare hand gesture recognition while there is no restriction on the types of gestures. Observations and results have confirmed that this research work can be used to remotely control a robotic hand using hand gestures. The system developed here is also able to recognize hand gestures in different lighting conditions. The pre-processing is performed by developing an image-cropping algorithm that ensures only the area of interest is included in the segmented image. The segmented image is compared with a predefined gesture set which must be installed in the recognition system. These images are stored and feature vectors are extracted from them. These feature vectors are subsequently presented using an orientation histogram, which provides a view of the edges in the form of frequency. Thereby, if the same gesture is shown twice in different lighting intensities, both repetitions will map to the same gesture in the stored data. The mapping of the segmented image's orientation histogram is firstly done using the Euclidian distance method. Secondly, the supervised neural network is trained for the same, producing better recognition results. An approach to controlling electro-mechanical robotic hands using dynamic hand gestures is also presented using a robot simulator. Such robotic hands have applications in commercial, military or emergency operations where human life cannot be risked. For such applications, an artificial robotic hand is required to perform real-time operations. This robotic hand should be able to move its fingers in the same manner as a human hand. For this purpose, hand geometry parameters are obtained using a webcam and also using KINECT. The parameter detection is direction invariant in both methods. Once the hand parameters are obtained, the fingers? angle information is obtained by performing a geometrical analysis. An artificial neural network is also implemented to calculate the angles. These two methods can be used with only one hand, either right or left. A separate method that is applicable to both hands simultaneously is also developed and fingers angles are calculated. The contents of this book will be useful for researchers and professional engineers working on robotic arm/hand systems. Read more...

This Edited Volume gathers a selection of refereed and revised papers originally presented at the Third International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS’17), held on September 13–16, 2017 in Manipal, India. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. Taken together, they offer a valuable resource for all researchers and scientists engaged in the various fields of signal processing and related areas.

this file contains:Advanced Deep Learning with Keras_ Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more (2018, Packt Publishing.pdf Deep Reinforcement Learning for Wireless Networks (2019, Springer International Publishing).pdf Deep Reinforcement Learning Hands-On_ Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more.pdf Hands-On Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).epub Hands-On Reinforcement Learning with Python_ Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow (2018, Packt Publishing).pdf Keras Reinforcement Learning Projects (2018, Packt Publishing).epub Keras Reinforcement Learning Projects (2018, Packt Publishing).pdf Practical Reinforcement Learning Develop self-evolving, intelligent agents with OpenAI Gym, Python and Java.pdf Python Reinforcement Learning Projects - 2018.pdf Reinforcement Learning for Optimal Feedback Control (2018, Springer International Publishing).pdf Reinforcement Learning with TensorFlow_ A beginner’s guide to designing self-learning systems with TensorFlow and OpenAI Gym (2018, Packt Publishing).pdf Reinforcement Learning _ With Open AI, TensorFlow and Keras Using Python-Apress (2018).pdf Reinforcement Learning_ An Introduction (2018, The MIT Press).pdf Simulation-Based Optimization_ Parametric Optimization Techniques and Reinforcement Learning (2015, Springer US).pdf Statistics for Machine Learning_ Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R-Packt Publishing (2017).pdf Tensorflow for Deep Learning_ From Linear Regression to Reinforcement Learning (2018, O'Reilly Media).pdf

this file contains:Ensemble Machine Learning Cookbook - 2019.pdf Hands-On Artificial Intelligence for IoT - 2019.pdf Home Automation with Raspberry Pi_ Projects Using Google Home, Amazon Echo, and Other Intelligent Personal Assistants (2019, McGraw-Hill Education).pdf Intelligent Projects Using Python - 2019.pdf Machine Learning and AI for Healthcare. Big Data for improved Health Outcomes (2019, Apress).pdf Practical Machine Learning and Image Processing - 2019.pdf Practical Python AI Projects.pdf Practical Recommender Systems (2019, Manning Publications).pdf Python Artificial Intelligence Projects for Beginners_ Get up and running with Artificial Intelligence using 8 smart and exciting AI applications (2018, Packt Publishing).pdf Python Machine Learning Blueprints 2nd - 2019.pdf Python Reinforcement Learning Projects - 2018.pdf Python Robotics Projects_ Build smart and collaborative robots using Python (2018, Packt Publishing).pdf Python_ Beginner’s Guide to Artificial Intelligence (2018, Packt Publishing).epub Python_ Beginner’s Guide to Artificial Intelligence (2018, Packt Publishing).pdf Pytorch Recipes_ A Problem-Solution Approach (2019, Apress).pdf Simple Machine Learning for Programmers - 2018.pdf TensorFlow 2 Machine Learning Cookbook (2018, Packt).epub TensorFlow 2 Machine Learning Cookbook (2018, Packt).pdf

Nowadays, monitoring systems play a crucial role in any IT environment. They are extensively used to not only measure your system's performance, but also to forecast capacity issues. This is where Zabbix, one of the most popular monitoring solutions for networks and applications, comes into the picture. With an efficient monitoring system in place, you'll be able to foresee when your infrastructure runs under capacity and react accordingly. Due to the critical role a monitoring system plays, it is fundamental to implement it in the best way from its initial setup. This avoids misleading, confusing, or, even worse, false alarms that can disrupt an efficient and healthy IT department.

this file contains:A Mathematica Primer for Physicists-CRC Press (2018).pdf An Elementary Introduction to the Wolfram Language 2ed.pdf An Engineer's guide to Mathematica.pdf An Introduction to Programming with mma.pdf Classical Mechanics with Mathematica?-Birkh?user (2018).pdf CRC standard curves and surfaces with Mathematica-CRC Press (2016).pdf Dynamical Systems with Applications Using Mathematica.pdf Essentials of Programming in Mathematica.pdf Foundations of Fluid Mechanics with Applications Problem Solving Using Mathematica.pdf Geographical Models with Mathematica- ISTE Press - Elsevier (2017).pdf Geometric Optics_ Theory and Design of Astronomical Optical Systems Using Mathematica.pdf Group Theory in Solid State Physics and Photonics Problem Solving with Mathematica.pdf Groups and Manifolds_ Lectures for Physicists with Examples in Mathematica (2017, de Gruyter).pdf HANDS-ON START TO WOLFRAM 2016.pdf Introduction to mma with Applications.pdf Irreducibility and Computational Equivalence 10 Years After Wolfram's A New Kind of Science.pdf Mathematica Beyond Mathematics. The Wolfram Language in the Real World.pdf Mathematica by Example 5 Edition-Academic Press (2017).pdf Mathematica介绍及数学建模中的应用.pdf mma for Bioinformatics. A Wolfram Language Approach to Omics-Springer (2018).pdf Molecular Physical Chemistry_ A Computer-based Approach using Mathematica? and Gaussian-Springer International Publishing (2017).pdf Raspbian OS Programming with the Raspberry Pi_ IoT Projects with Wolfram, Mathematica, and Scratch-Apress (2019).pdf Schaum's Outline of Mathematica and the wolfram language.pdf

This book is designed to help students and professionals who use mathematics in their daily routine learn Mathematica®, a computer system designed to perform complex mathematical calculations. My approach is simple: learn by example. Along with easy to read descriptions of the most widely used commands, I have included a collection of over 750 examples and solved problems, each specifically designed to illustrate an important feature of the Mathematica software. No attempt has been made to discuss all the capabilities of Mathematica. As this is a book designed for first-time users at the undergraduate level, I have included those commands and options that are most commonly used in algebra, trigonometry, calculus, differential equations, and linear algebra. Most examples and solved problems are short and to the point. Comments have been included, where appropriate, to clarify what might be confusing to the reader. The reader is encouraged not only to replicate the output shown in the text, but to make modifications and investigate the resulting effect upon the output. I have found this to be the most effective way to learn the syntax and capabilities of this truly unique program. Over the years Mathematica has undergone a significant number of changes. This third edition incorporates all the changes in the command descriptions, examples, and solved problems. In addition, a comprehensive list of commands used in the book together with their descriptions is conveniently located in the appendix. The first three chapters serve as an introduction to the syntax and style of Mathematica. The structure of the remainder of the book is such that the reader need only be concerned with those chapters of interest to him or her. If, on occasion, a command is encountered that has been discussed in a previous chapter, the index may be used to conveniently locate the command’s description. Without a doubt you will be impressed with Mathematica’s capabilities. It is my sincere hope that you will use the power built into this software to investigate the wonders of mathematics in a way that would have been impossible just a few years ago.

A very wide selection of excellent books are available to the reader interested in geometric optics. Roughly speaking, these texts can be divided into three main classes. In the first class (see, for instance, [1–14]), we find books that present the theoretical aspects of the subject, usually starting from the Lagrangian and Hamiltonian formulations of geometric optics. These texts analyze the relations between geometric optics, mechanics, partial differential equations, and the wave theory of optics. The second class comprises books that focus on the applications of this theory to optical instruments. In these books some essential formulae, which are reported without proofs, are used to propose exact or approximate solutions to real-world problems (an excellent example of this class is represented by [26]). The third class contains books that approach the subject in a manner that is intermediate between the first two classes (see, for instance, [15–21]). The aim of this book, which could be placed in the third class, is to provide the reader with the mathematical background needed to design many optical combinations that are used in astronomical telescopes and cameras.1 The results presented here were obtained by using a different approach to third-order aberration theory as well as the extensive use of the software package Mathematica®. The third-order approach to third-order aberration theory adopted in this book is based on Fermat’s principle and on the use of particular optical paths (not rays) termed stigmatic paths. This approach makes it easy to derive the third-order aberration formulae. In this way, the reader is able to understand and handle the formulae required to design optical combinations without resorting to the much more complex Lagrangian and Hamiltonian formalisms and Seidel’s relations. On the other hand, the Lagrangian and Hamiltonian formalisms have unquestionable theoretical utility considering their important applications in optics, mechanics, and the theory of partial differential equations. For this reason the Lagrangian and Hamiltonian optics are widely discussed in Chapters 10–12.

This book aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground. While writing the book, we had to make difficult, and sometimes uncomfortable, choices on what material to leave out. For a beginner reader, we hope the book will provide a strong foundation in the basics and a glimpse of what is possible. Machine learning, and deep learning in particular, is an experiential discipline, as opposed to an intellectual science. The generous endtoend code examples in each chapter invite you to partake in that experience. When we began working on the book, we started with PyTorch 0.2. The examples were revised with each PyTorch update from 0.2 to 0.4. P yTorch 1.0 is due to release around when this book comes out. The code examples in the book are PyTorch 0.4–compliant and should work as they are with the upcoming PyTorch 1.0 release. A note regarding the style of the book. We have intentionally avoided mathematics in most places, not because deep learning math is particularly difficult (it is not), but because it is a distraction in many situations from the main goal of this book—to empower the beginner learner. Likewise, in many cases, both in code and text, we have favored exposition over succinctness. Advanced readers and experienced programmers will likely see ways to tighten up the code and so on, but our choice was to be as explicit as possible so as to reach the broadest of the audience that we want to reach.

This open access book was prepared as a Final Publication of the COST Action IC1304 “Autonomous Control for a Reliable Internet of Services (ACROSS)”. The book contains 14 chapters and constitutes a show-case of the main outcome of the Action in line with its scientific goals. It will serve as a valuable reference for undergraduate and post-graduate students, educators, faculty members, researchers, engineers, and research strategists working in this field. The explosive growth of the Internet has fundamentally changed the global society. The emergence of concepts like SOA, SaaS, PaaS, IaaS, NaaS, and Cloud Computing in general has catalyzed the migration from the information-oriented Internet into an Internet of Services (IoS). This has opened up virtually unbounded possibilities for the creation of new and innovative services that facilitate business processes and improve the quality of life. However, this also calls for new approaches to ensuring the quality and reliability of these services. The objective of this book is, by applying a systematic approach, to assess the state-of-the-art and consolidate the main research results achieved in this area.