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Beginning SOLID Principles and Design Patterns for ASP.NET Developers
by Bipin JoshiThis book teaches you all the essential knowledge required to learn and apply time-proven SOLID principles of object-oriented design and important design patterns in ASP. NET 5 (recently renamed ASP. NET Core 1. 0) applications. You will learn to write server-side as well as client-side code that makes use of proven practices and patterns. SOLID is an acronym used to describe five basic principles of good object-oriented design--Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation and Dependency Inversion. This book covers all five principles and illustrates how they can be used in ASP. NET 5 applications. Design Patterns are time proven solutions to commonly occurring software design problems. The most well-known catalog of design patterns comes from Erich Gamma, Richard Helm, Ralph Johnson and John Vlissides, the so-called GoF patterns (Gang of Four patterns). This book contains detailed descriptions of how to apply Creational, Structural and Behavioral GoF design patterns along with some Patterns of Enterprise Application Architecture. Popular JavaScript patterns are covered, along with working examples of all these patterns in ASP. NET 5 and C# are included. * How to apply SOLID principles to ASP. NET 5 applications What you'll learn * How to use Gang of Four (GoF) design patterns in ASP. NET 5 applications * Techniques for applying Patterns of Enterprise Application Architecture cataloged by Martin Fowler in ASP. NET 5 applications * How to organize code and apply design patterns in JavaScript Who this book is for This book is for ASP. NET developers familiar with ASP. NET 5, C# and Visual Studio. Table of Contents 1. Overview of SOLID Principles and Design Patterns 2. SOLID Principles 3. Creational Patterns - Singleton, Factory Method and Prototype 4. Creational Patterns - Abstract Factory and Builder 5. Structural Patterns - Adapter, Bridge, Composite and Decorator 6. Structural Patterns - Façade, Flyweight and Proxy 7. Behavioral Patterns - Chain of Responsibility, Command, Interpreter and Iterator 8. Behavioral Patterns - Mediator, Memento and Observer 9. Behavioral Patterns - State, Strategy, Template Method and Visitor 10. Patterns of Enterprise Application Architecture - Repository, Unit of Work, Lazy Load and Service Layer 11. JavaScript Code Organization Techniques and Patterns
Beginning XML with C# 7: XML Processing and Data Access for C# Developers
by Bipin JoshiMaster the basics of XML as well as the namespaces and objects you need to know in order to work efficiently with XML. You'll learn extensive support for XML in everything from data access to configuration, from raw parsing to code documentation. You will see clear, practical examples that illustrate best practices in implementing XML APIs and services as part of your C#-based Windows 10 applications. Beginning XML with C# updates Bipin Joshi's one-of-a-kind title to the new C# 7 programming language and . NET 4. 7 Framework releases. In this update, you'll discover the tight integration of XML with ADO. NET and LINQ as well as additional . NET support for today's RESTful web services and microservices. Written by a Microsoft Certified trainer and developer, this book demystifies everything to do with XML and C# 7. What You'll Learn Discover how XML works with the . NET Framework Read, write, access, validate, and manipulate XML documents Transform XML with XSLT Use XML serialization and web services Combine XML in ADO. NET and SQL Server Create services using Windows Communication Foundation Work with LINQ Use XML with C# in Azure and more Who This Book Is For Those with experience in C# and . NET new to the nuances of using XML. Some XML experience is helpful.
Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
by Gauri JoshiThis book discusses state-of-the-art stochastic optimization algorithms for distributed machine learning and analyzes their convergence speed. The book first introduces stochastic gradient descent (SGD) and its distributed version, synchronous SGD, where the task of computing gradients is divided across several worker nodes. The author discusses several algorithms that improve the scalability and communication efficiency of synchronous SGD, such as asynchronous SGD, local-update SGD, quantized and sparsified SGD, and decentralized SGD. For each of these algorithms, the book analyzes its error versus iterations convergence, and the runtime spent per iteration. The author shows that each of these strategies to reduce communication or synchronization delays encounters a fundamental trade-off between error and runtime.
Blockchain – ICBC 2019: Second International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, June 25–30, 2019, Proceedings (Lecture Notes in Computer Science #11521)
by James Joshi Surya Nepal Qi Zhang Liang-Jie ZhangThis book constitutes the refereed proceedings of the Second International Conference on Blockchain, ICBC 2019, held as part of the Services Conference Federation, SCF 2019, in San Diego, CA, USA, in June 2019. The 13 full papers and 2 short papers presented were carefully reviewed and selected from 29 submissions. The papers cover a wide range of topics in blockchain technologies, platforms, solutions and business models such as new blockchain architecture, platform constructions, blockchain development and blockchain services technologies, as well as standards, and blockchain services innovation lifecycle including enterprise modeling, business consulting, solution creation, services orchestration, services optimization, services management, services marketing, business process integration and management.
Mastering Chef
by Mayank JoshiIf you have used Chef before and are interested in automation of infrastructure and want to develop your own tools to manage large-scale infrastructures, then this book is for you.
Machine Learning for Advanced Functional Materials
by Nirav Joshi Vinod Kushvaha Priyanka MadhushriThis book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.
Hands-On Artificial Intelligence with Java for Beginners: Build intelligent apps using machine learning and deep learning with Deeplearning4j
by Nisheeth JoshiBuild, train, and deploy intelligent applications using Java librariesKey FeaturesLeverage the power of Java libraries to build smart applicationsBuild and train deep learning models for implementing artificial intelligenceLearn various algorithms to automate complex tasksBook DescriptionArtificial intelligence (AI) is increasingly in demand as well as relevant in the modern world, where everything is driven by technology and data. AI can be used for automating systems or processes to carry out complex tasks and functions in order to achieve optimal performance and productivity.Hands-On Artificial Intelligence with Java for Beginners begins by introducing you to AI concepts and algorithms. You will learn about various Java-based libraries and frameworks that can be used in implementing AI to build smart applications. In addition to this, the book teaches you how to implement easy to complex AI tasks, such as genetic programming, heuristic searches, reinforcement learning, neural networks, and segmentation, all with a practical approach.By the end of this book, you will not only have a solid grasp of AI concepts, but you'll also be able to build your own smart applications for multiple domains.What you will learnLeverage different Java packages and tools such as Weka, RapidMiner, and Deeplearning4j, among othersBuild machine learning models using supervised and unsupervised machine learning techniquesImplement different deep learning algorithms in Deeplearning4j and build applications based on themStudy the basics of heuristic searching and genetic programmingDifferentiate between syntactic and semantic similarity among textsPerform sentiment analysis for effective decision making with LingPipeWho this book is forHands-On Artificial Intelligence with Java for Beginners is for Java developers who want to learn the fundamentals of artificial intelligence and extend their programming knowledge to build smarter applications.
Data Storytelling and Visualization with Tableau: A Hands-on Approach
by Prachi Manoj Joshi Parikshit Narendra MahalleWith the tremendous growth and availability of the data, this book covers understanding the data, while telling a story with visualization including basic concepts about the data, the relationship and the visualizations. All the technical details that include installation and building the different visualizations are explained in a clear and systematic way. Various aspects pertaining to storytelling and visualization are explained in the book through Tableau. Features Provides a hands-on approach in Tableau in a simplified manner with steps Discusses the broad background of data and its fundamentals, from the Internet of Everything to analytics Emphasizes the use of context in delivering the stories Presents case studies with the building of a dashboard Presents application areas and case studies with identification of the impactful visualization This book will be helpful for professionals, graduate students and senior undergraduate students in Manufacturing Engineering, Civil and Mechanical Engineering, Data Analytics and Data Visualization.
Artificial Intelligence with Python
by Prateek JoshiBuild real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book • Step into the amazing world of intelligent apps using this comprehensive guide • Enter the world of Artificial Intelligence, explore it, and create your own applications • Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn • Realize different classification and regression techniques • Understand the concept of clustering and how to use it to automatically segment data • See how to build an intelligent recommender system • Understand logic programming and how to use it • Build automatic speech recognition systems • Understand the basics of heuristic search and genetic programming • Develop games using Artificial Intelligence • Learn how reinforcement learning works • Discover how to build intelligent applications centered on images, text, and time series data • See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.
OpenCV with Python By Example
by Prateek JoshiBuild real-world computer vision applications and develop cool demos using OpenCV for Python <P><P> About This Book<P> * Learn how to apply complex visual effects to images using geometric transformations and image filters<P> * Extract features from an image and use them to develop advanced applications<P> * Build algorithms to help you understand the image content and perform visual searches<P> Who This Book Is For<P> This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on. <P> What You Will Learn <P> * Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image<P> * Detect and track various body parts such as the face, nose, eyes, ears, and mouth<P> * Stitch multiple images of a scene together to create a panoramic image<P> * Make an object disappear from an image * Identify different shapes, segment an image, and track an object in a live video<P> * Recognize an object in an image and build a visual search engine<P> * Reconstruct a 3D map from images<P> * Build an augmented reality application In Detail Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. Style and approach This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.
OpenCV with Python By Example
by Prateek Joshi<P><P>Build real-world computer vision applications and develop cool demos using OpenCV for Python <P><P>About This Book <P><P>Learn how to apply complex visual effects to images using geometric transformations and image filters <P><P>Extract features from an image and use them to develop advanced applications <P><P>Build algorithms to help you understand the image content and perform visual searches <P><P>Who This Book Is For <P><P>This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on. <P><P>What You Will Learn <P><P>Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image <P><P>Detect and track various body parts such as the face, nose, eyes, ears, and mouth <P><P>Stitch multiple images of a scene together to create a panoramic image <P><P>Make an object disappear from an image <P><P>Identify different shapes, segment an image, and track an object in a live video <P><P>Recognize an object in an image and build a visual search engine <P><P>Reconstruct a 3D map from images <P><P>Build an augmented reality application <P><P>In Detail <P><P>Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. <P><P>This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. <P><P>This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. <P><P>The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV nd their actual implementation.
Python: Real World Machine Learning
by Prateek JoshiLearn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!
Python Machine Learning Cookbook
by Prateek JoshiThis book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code.
Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x and TensorFlow 2, 2nd Edition
by Prateek Joshi Alberto ArtasanchezNew edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x and TensorFlow 2, with seven new chapters that cover RNNs, AI & Big Data, fundamental use cases, chatbots, and more. Key Features Completely updated and revised to Python 3.x, and TensorFlow 2 Seven new chapters that include AI on the cloud, RNNs and DL models, feature engineering, the machine learning data pipeline, and more New author with 25 years of experience in artificial intelligence across multiple industries and enterprise domains Book Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x and TensorFlow 2. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Understand RNNs and various DL models Who this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.
Python Machine Learning Cookbook: Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets, 2nd Edition
by Prateek Joshi Giuseppe CiaburroDiscover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch Key Features Learn and implement machine learning algorithms in a variety of real-life scenarios Cover a range of tasks catering to supervised, unsupervised and reinforcement learning techniques Find easy-to-follow code solutions for tackling common and not-so-common challenges Book Description This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks. With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning. By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples. What you will learn Use predictive modeling and apply it to real-world problems Explore data visualization techniques to interact with your data Learn how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Get well versed with reinforcement learning, automated ML, and transfer learning Work with image data and build systems for image recognition and biometric face recognition Use deep neural networks to build an optical character recognition system Who this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and Python programmers who want to solve real-world challenges using machine-learning techniques and algorithms. If you are facing challenges at work and want ready-to-use code solutions to cover key tasks in machine learning and the deep learning domain, then this book is what you need. Familiarity with Python programming and machine learning concepts will be useful.
Learn OpenCV 4 by Building Projects: Build real-world computer vision and image processing applications with OpenCV and C++, 2nd Edition (02 RRP including tax)
by Prateek Joshi David Millán Escrivá Vinícius G. MendonçaExplore OpenCV 4 to create visually appealing cross-platform computer vision applicationsKey FeaturesUnderstand basic OpenCV 4 concepts and algorithmsGrasp advanced OpenCV techniques such as 3D reconstruction, machine learning, and artificial neural networksWork with Tesseract OCR, an open-source library to recognize text in imagesBook DescriptionOpenCV is one of the best open source libraries available, and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation. Whether you’re completely new to computer vision, or have a basic understanding of its concepts, Learn OpenCV 4 by Building Projects – Second edition will be your guide to understanding OpenCV concepts and algorithms through real-world examples and projects. You’ll begin with the installation of OpenCV and the basics of image processing. Then, you’ll cover user interfaces and get deeper into image processing. As you progress through the book, you'll learn complex computer vision algorithms and explore machine learning and face detection. The book then guides you in creating optical flow video analysis and background subtraction in complex scenes. In the concluding chapters, you'll also learn about text segmentation and recognition and understand the basics of the new and improved deep learning module.By the end of this book, you'll be familiar with the basics of Open CV, such as matrix operations, filters, and histograms, and you'll have mastered commonly used computer vision techniques to build OpenCV projects from scratch.What you will learnInstall OpenCV 4 on your operating systemCreate CMake scripts to compile your C++ applicationUnderstand basic image matrix formats and filtersExplore segmentation and feature extraction techniquesRemove backgrounds from static scenes to identify moving objects for surveillanceEmploy various techniques to track objects in a live videoWork with new OpenCV functions for text detection and recognition with TesseractGet acquainted with important deep learning tools for image classificationWho this book is forIf you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, Learn OpenCV 4 by Building Projects for you. Prior knowledge of C++ will help you understand the concepts covered in this book.
OpenCV: Computer Vision Projects with Python
by Prateek Joshi Joseph Howse Michael BeyelerGet savvy with OpenCV and actualize cool computer vision applications About This Book * Use OpenCV's Python bindings to capture video, manipulate images, and track objects * Learn about the different functions of OpenCV and their actual implementations. * Develop a series of intermediate to advanced projects using OpenCV and Python Who This Book Is For This learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV. What You Will Learn * Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu * Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games * Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image * Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor * Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques * Detect and recognize street signs using a cascade classifier and support vector machines (SVMs) * Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs * Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * OpenCV Computer Vision with Python by Joseph Howse * OpenCV with Python By Example by Prateek Joshi * OpenCV with Python Blueprints by Michael Beyeler Style and approach This course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this comprehensive course, you'll learn to create computer vision applications from scratch to finish and more!.
Python: Real World Machine Learning
by Prateek Joshi Luca Massaron John Hearty Bastiaan Sjardin Alberto BoschettiLearn to solve challenging data science problems by building powerful machine learning models using Python About This Book * Understand which algorithms to use in a given context with the help of this exciting recipe-based guide * This practical tutorial tackles real-world computing problems through a rigorous and effective approach * Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn * Use predictive modeling and apply it to real-world problems * Understand how to perform market segmentation using unsupervised learning * Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test * Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms * Increase predictive accuracy with deep learning and scalable data-handling techniques * Work with modern state-of-the-art large-scale machine learning techniques * Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: * Python Machine Learning Cookbook by Prateek Joshi * Advanced Machine Learning with Python by John Hearty * Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!
Advances in Engineering Design: Select Proceedings of FLAME 2020 (Lecture Notes in Mechanical Engineering)
by Preeti Joshi Shakti S. Gupta Anoop Kumar Shukla Sachin Singh GautamThis book presents select proceedings of the International Conference on Future Learning Aspects of Mechanical Engineering (FLAME 2020). The book focuses on latest research in mechanical engineering design and covers topics such as computational mechanics, finite element modeling, computer aided engineering and analysis, fracture mechanics, and vibration. The book brings together different aspects of engineering design and the contents will be useful for researchers and professionals working in this field.
Fundamentals of Network Forensics: A Research Perspective (Computer Communications and Networks)
by R.C. Joshi Emmanuel S. PilliThis timely text/reference presents a detailed introduction to the essential aspects of computer network forensics. The book considers not only how to uncover information hidden in email messages, web pages and web servers, but also what this reveals about the functioning of the Internet and its core protocols. This, in turn, enables the identification of shortcomings and highlights where improvements can be made for a more secure network. Topics and features: provides learning objectives in every chapter, and review questions throughout the book to test understanding; introduces the basic concepts of network process models, network forensics frameworks and network forensics tools; discusses various techniques for the acquisition of packets in a network forensics system, network forensics analysis, and attribution in network forensics; examines a range of advanced topics, including botnet, smartphone, and cloud forensics; reviews a number of freely available tools for performing forensic activities.
Microsoft Enterprise Library 5.0
by Sachin JoshiThis is a step-by-step tutorial in which a chapter is dedicated to each Application Block of the Microsoft Enterprise Library 5.0. We will develop small applications to implement the functions in each Application Block all through the book.If you are a Programmer, Consultant, or an Associate Architect, who is interested in developing Enterprise applications, this book is for you. We assume that you already have a good knowledge of Microsoft .NET framework and the C# programming language.
Cyber Warfare, Security and Space Research: First International Conference, SpacSec 2021, Jaipur, India, December 9–10, 2021, Revised Selected Papers (Communications in Computer and Information Science #1599)
by Sandeep Joshi Amit Kumar Bairwa Amita Nandal Milena Radenkovic Cem AvsarThis book sonstitutes selected papers from the first International Conference on Cyber Warfare, Security and Space Research, SpacSec 2021, held in Jaipur, India, in December 2021.The 19 full and 6 short papers were thoroughly reviewed and selected from the 98 submissions. The papers present research on cyber warfare, cyber security, and space research area, including the understanding of threats and risks to systems, the development of a strong innovative culture, and incident detection and post-incident investigation.
Additive Manufacturing with Metals: Design, Processes, Materials, Quality Assurance, and Applications
by Sanjay Joshi Richard P. Martukanitz Abdalla R. Nassar Pan MichalerisThis textbook and reference provides a comprehensive treatment of additive manufacturing (AM) for metals, including design and digital work flows, process science and reliability, metallic systems, quality assurance, and applications. The book is rooted in the fundamental science necessary to develop and understand AM technologies, as well as the application of engineering principles covering several disciplines to successfully exploit this important technology. As additive manufacturing of metals is the fastest growing subset of this transformative technology, with the potential to make the widest impact to industrial production, Metals Additive Manufacturing: Design, Processes, Materials, Quality Assurance, and Applications is ideal for students in a range of engineering disciplines and practitioners working in aerospace, automotive, medical device manufacturing industries.
Low Cost Manufacturing Technologies: Proceedings of NERC 2022
by Shrikrishna Nandkishor Joshi Uday Shanker Dixit R. K. Mittal Swarup BagThis book is on various advanced, simple, and novel techniques being used and developed in the area of manufacturing processes. Manufacturing sector is one of the important areas which help to improve the economy of our nation. It not only generates employment opportunities but also makes us self-reliant (aatma nirbhar). In line with this important agenda of Government of India, this track envisages high-quality research contributions in the field of low-cost manufacturing technologies. It comprises the research and development studies on the various factors that influence the cost of manufacturing of product or system. The factors are materials, manufacturing processes, material handling processes, skilled manpower, quality control technologies, effective communication, and use of artificial intelligence techniques. The papers are on both numerical and experimental research works related to these aspects.
Digital Finance, Bits and Bytes: The Road Ahead
by Vasant Chintaman JoshiThe book encompasses the broad field of e-Finance and its transformation. After reviewing the developments in the economic and the technology fields, it examines how the insurance, banking, and securities trading firms are bringing about the digital revolution and adapting in the same breath to the changed socio-economic environment. Add to it, the “Rogue Elements”, the field of cyber crimes is covered on a priority basis. The book also covers the inevitable changes in fields of HR and Marketing and the crucial role of the regulators. Looked at through the eyes of Corporate Planner, the book does provide a road map for the financial institutions (FIs).