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Digital Signal Processing and Applications with the OMAP - L138 eXperimenter
Teaches digital signal processing concepts via hands-on examples The OMAP-L138 eXperimenter is the latest inexpensive DSP development system to be adopted by the Texas Instruments University Program. The OMAP-L138 processor contains both ARM and DSP cores and is aimed at portable and mobile multimedia applications. This book concentrates on the demonstration of real-time DSP algorithms implemented on its C6748 DSP core. Digital Signal Processing and Applications with the OMAP-L138 eXperimenter provides an extensive and comprehensive set of program examples to aid instructors in teaching DSP in a laboratory using audio frequency signals—making it an ideal text for DSP courses at senior undergraduate and postgraduate levels. Subjects covered include polling-based, interrupt-based, and DMA-based I/O methods, and how real-time programs may be run using the board support library (BSL), the DSP/BIOS real-time operating system, or the DSP/BIOS Platform Support Package. Chapters include: Analog input and output with the OMAP-L138 eXperimenter Finite impulse response filters Infinite impulse response filters Fast Fourier transform Adaptive filters DSP/BIOS and platform support package Each chapter begins with a review of background theory and then presents a number of real-time program examples to reinforce understanding of that theory and to demonstrate the use of the OMAP-L138 eXperimenter and Texas Instruments Code Composer Studio integrated development environment.
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Getting Started with Forex Trading Using Python
Discover the inner workings of today's forex market, the essential risks in forex algo trading, and how to mitigate themKey FeaturesBuild trading applications with research and without advanced Python programming skillsDive into professional fx trading while enhancing your trading apps to be more accurateDevelop simple yet efficient backtesting applications to help keep your expectations realisticBook DescriptionAlgorithm-based trading is a popular choice for Python programmers due to its apparent simplicity. However, very few traders get the results they want, partly because they aren't able to capture the complexity of the factors that influence the market. Getting Started with Forex Trading Using Python helps you understand the market and build an application that reaps desirable results. The book is a comprehensive guide to everything that is market-related: data, orders, trading venues, and risk. From the programming side, you'll learn the general architecture of trading applications, systemic risk management, de-facto industry standards such as FIX protocol, and practical examples of using simple Python codes. You'll gain an understanding of how to connect to data sources and brokers, implement trading logic, and perform realistic tests. Throughout the book, you'll be encouraged to further study the intricacies of algo trading with the help of code snippets. By the end of this book, you'll have a deep understanding of the fx market from the perspective of a professional trader. You'll learn to retrieve market data, clean it, filter it, compress it into various formats, apply trading logic, emulate the execution of orders, and test the trading app before trading live.What you will learnExplore the forex market organization and operationsUnderstand the sources of alpha and the concept of algo tradingGet a grasp on typical risks and ways to mitigate themUnderstand fundamental and technical analysisConnect to data sources and check the integrity of market dataUse API and FIX protocol to send ordersTranslate trading ideas into codeRun reliable backtesting emulating real-world market conditionsWho this book is forThis book is for financial traders and python developers who are interested in forex trading. Academic researchers looking to focus on practical applications will find this book useful. This book can also help established fx market professionals who want to take the first steps in algo trading. Familiarity with Python and object-oriented programming within the scope of an online course or self-study is a must. Knowledge of network protocols and interfaces is a plus but not a prerequisite, as is specific knowledge about markets and trading.
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Building Node Applications with MongoDB and Backbone
The enthusiasm behind Node doesn't just reflect the promise of server-side JavaScript. Developers also have the potential to create elegant applications with this open source framework that are much easier to maintain. Follow author Mike Wilson as he builds a social network application oriented toward real-time updates. Through this, the book shows you how to build complete applications that combine the strengths of Node, the MongoDB scalable storage solution, and the Backbone toolkit for clean application design in the browser. Gain examples of all three tools in real development situations Learn how to build clean, manageable code Use a complete application toolkit from start to finish, server to client Understand the power of JavaScript in a variety of contexts
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R 611.00
Learn Quantum Computing with Python and IBM Quantum Experience
A step-by-step guide to learning the implementation and associated methodologies in quantum computing with the help of the IBM Quantum Experience, Qiskit, and Python that will have you up and running and productive in no timeKey FeaturesDetermine the difference between classical computers and quantum computersUnderstand the quantum computational principles such as superposition and entanglement and how they are leveraged on IBM Quantum Experience systemsRun your own quantum experiments and applications by integrating with QiskitBook DescriptionIBM Quantum Experience is a platform that enables developers to learn the basics of quantum computing by allowing them to run experiments on a quantum computing simulator and a real quantum computer. This book will explain the basic principles of quantum mechanics, the principles involved in quantum computing, and the implementation of quantum algorithms and experiments on IBM's quantum processors. You will start working with simple programs that illustrate quantum computing principles and slowly work your way up to more complex programs and algorithms that leverage quantum computing. As you build on your knowledge, you'll understand the functionality of IBM Quantum Experience and the various resources it offers. Furthermore, you'll not only learn the differences between the various quantum computers but also the various simulators available. Later, you'll explore the basics of quantum computing, quantum volume, and a few basic algorithms, all while optimally using the resources available on IBM Quantum Experience. By the end of this book, you'll learn how to build quantum programs on your own and have gained practical quantum computing skills that you can apply to your business.What you will learnExplore quantum computational principles such as superposition and quantum entanglementBecome familiar with the contents and layout of the IBM Quantum ExperienceUnderstand quantum gates and how they operate on qubitsDiscover the quantum information science kit and its elements such as Terra and AerGet to grips with quantum algorithms such as Bell State, Deutsch-Jozsa, Grover's algorithm, and Shor's algorithmHow to create and visualize a quantum circuitWho this book is forThis book is for Python developers who are looking to learn quantum computing and put their knowledge to use in practical situations with the help of IBM Quantum Experience. Some background in computer science and high-school-level physics and math is required.
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Introduction to Bootstrap Methods with Applications to R
A comprehensive introduction to bootstrap methods in the R programming environment Bootstrap methods provide a powerful approach to statistical data analysis, as they have more general applications than standard parametric methods. An Introduction to Bootstrap Methods with Applications to R explores the practicality of this approach and successfully utilizes R to illustrate applications for the bootstrap and other resampling methods. This book provides a modern introduction to bootstrap methods for readers who do not have an extensive background in advanced mathematics. Emphasis throughout is on the use of bootstrap methods as an exploratory tool, including its value in variable selection and other modeling environments. The authors begin with a description of bootstrap methods and its relationship to other resampling methods, along with an overview of the wide variety of applications of the approach. Subsequent chapters offer coverage of improved confidence set estimation, estimation of error rates in discriminant analysis, and applications to a wide variety of hypothesis testing and estimation problems, including pharmaceutical, genomics, and economics. To inform readers on the limitations of the method, the book also exhibits counterexamples to the consistency of bootstrap methods. An introduction to R programming provides the needed preparation to work with the numerous exercises and applications presented throughout the book. A related website houses the book's R subroutines, and an extensive listing of references provides resources for further study. Discussing the topic at a remarkably practical and accessible level, An Introduction to Bootstrap Methods with Applications to R is an excellent book for introductory courses on bootstrap and resampling methods at the upper-undergraduate and graduate levels. It also serves as an insightful reference for practitioners working with data in engineering, medicine, and the social sciences who would like to acquire a basic understanding of bootstrap methods.
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Ontologies with Python
Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered. Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python.What You Will LearnUse Owlready2 to access and modify OWL ontologies in PythonPublish ontologies on dynamic websitesPerform automatic reasoning in PythonUse well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System)Integrate Python methods in OWL ontologies Who Is This Book ForBeginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.
R 1,254.00
Linear Algebra with Applications, Global Edition
This book is for junior/senior-level first courses in linear algebra and assumes calculus as a prerequisite. This thorough and accessible text, from one of the leading figures in the use of technology in linear algebra, gives students a challenging and broad understanding of the subject. The author infuses key concepts with their modern practical applications to offer students examples of how mathematics is used in the real world. Each chapter contains integrated worked examples and chapter tests. The book stresses the important roles geometry and visualisation play in understanding linear algebra.
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Developing Web Applications with Haskell and Yesod 2e
This fast-moving guide introduces web application development with Haskell and Yesod, a potent language/framework combination that supports high-performing applications that are modular, type-safe, and concise. Fully updated for Yesod 1.4, this second edition shows you how Yesod handles widgets, forms, persistence, and RESTful content. Author Michael Snoyman also introduces various Haskell tools to supplement your basic knowledge of the language. By the time you finish this book, you'll create a production-quality web application with Yesod's ready-to-use scaffolding. You'll also examine several real-world examples, including a blog, a wiki, a JSON web service, and a Sphinx search server.Build a simple application to learn Yesod's foundation datatype and Web Application Interface (WAI) Output HTML, CSS, and Javascript with Shakespearean template languages Get an indepth look at Yesod's core monads for producing cleaner, more modular code Probe Yesod's internal workings: learn the request handling process for a typical application Build forms on top of widgets by implementing the yesod-form declarative API Learn how Yesod and Haskell handle persistence and session data Serve an HTML page and a machine-friendly JSON page from the same URL
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Building Intelligent Cloud Applications
Serverless computing is radically changing the way we build and deploy applications. With cloud providers running servers and managing machine resources, companies now can focus solely on the application's business logic and functionality. This hands-on book shows experienced programmers how to build and deploy scalable machine learning and deep learning models using serverless architectures with Microsoft Azure.You'll learn step-by-step how to code machine learning into your projects using Python and pre-trained models that include tools such as image recognition, speech recognition, and classification. You'll also examine issues around deployment and continuous delivery including scaling, security, and monitoring.This book is divided into four parts:Cloud-based development: learn the basics of serverless computing with machine learning, functions as a service (FaaS), and the use of APIsAdding intelligence: create serverless applications using Azure Functions; learn how to use pre-built machine-learning and deep-learning modelsDeployment and continuous delivery: get up to speed with Azure Kubernetes Service, as well as Azure Security Center, and Azure MonitoringApplication examples: deliver data at the edge, build conversational interfaces, and use convolutional neural networks for image classification
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Learning Scientific Programming with Python
Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-point precision and algorithm stability, and extensive online resources support further study. This textbook represents a targeted package for students requiring a solid foundation in Python programming.
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Interpretable Machine Learning with Python
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable modelsKey FeaturesLearn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.What you will learnRecognize the importance of interpretability in businessStudy models that are intrinsically interpretable such as linear models, decision trees, and Naïve BayesBecome well-versed in interpreting models with model-agnostic methodsVisualize how an image classifier works and what it learnsUnderstand how to mitigate the influence of bias in datasetsDiscover how to make models more reliable with adversarial robustnessUse monotonic constraints to make fairer and safer modelsWho this book is forThis book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
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Statistics and Data Visualisation with Python
This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
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Practical Guide to Machine Learning, NLP, and Generative AI: Libraries, Algorithms, and Applications
This is an essential resource for beginners and experienced practitioners in machine learning. This comprehensive guide covers a broad spectrum of machine learning topics, starting with an in-depth exploration of popular machine learning libraries. Readers will gain a thorough understanding of Scikit-learn, TensorFlow, PyTorch, Keras, and other pivotal libraries like XGBoost, LightGBM, and CatBoost, which are integral for efficient model development and deployment.The book delves into various neural network architectures, providing readers with a solid foundation in understanding and applying these models. Beginning with the basics of the Perceptron and its application in digit classification, it progresses to more complex structures such as multilayer perceptrons for financial forecasting, radial basis function networks for air quality prediction, and convolutional neural networks (CNNs) for image classification. Additionally, the book covers recurrent neural networks (RNNs) and their variants like long short-term memory (LSTM) and gated recurrent units (GRUs), which are crucial for time-series analysis and sequential data applications.Supervised machine learning algorithms are meticulously explained, with practical examples to illustrate their application. The book covers logistic regression and its use in predicting sports outcomes, decision trees for plant classification, random forests for traffic prediction, and support vector machines for house price prediction. Gradient boosting machines and their applications in genomics, AdaBoost for bioinformatics data classification, and extreme gradient boosting (XGBoost) for churn prediction are also discussed, providing readers with a robust toolkit for various predictive tasks.Unsupervised learning algorithms are another significant focus of the book, introducing readers to techniques for uncovering hidden patterns in data. Hierarchical clustering for gene expression data analysis, principal component analysis (PCA) for climate predictions, and singular value decomposition (SVD) for signal denoising are thoroughly explained. The book also explores applications like robot navigation and network security, demonstrating the versatility of these techniques.Natural language processing (NLP) is comprehensively covered, highlighting its fundamental concepts and various applications. The book discusses the overview of NLP, its fundamental concepts, and its diverse applications such as chatbots, virtual assistants, clinical NLP applications, and social media analytics. Detailed sections on text pre-processing, syntactic analysis, machine translation, text classification, named entity recognition, and sentiment analysis equip readers with the knowledge to build sophisticated NLP models.The final chapters of the book explore generative AI, including generative adversarial networks (GANs) for image generation, variational autoencoders for vibrational encoder training, and autoregressive models for time series
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Python in Excel for Data Analytics
Learn how to use Python directly in Excel to clean data, build models, and create advanced visualizations. A practical guide for analysts who want to extend Excel without leaving the spreadsheet environment.Key FeaturesUse Python inside Excel for real analytical workflowsBuild visualizations, models, and simulations with easeCombine Excel structure with Python’s analytical powerBook DescriptionExcel is one of the most widely used tools for business analysis, but many analytical tasks quickly reach the limits of formulas and built-in features. Python in Excel changes that by allowing you to perform advanced analysis directly within the spreadsheet environment.This book shows how to combine Excel’s structure and usability with Python’s analytical power. You will learn how to move data between Excel and Python DataFrames, clean and transform datasets efficiently, and explore data using modern visualization techniques.As you progress, you will apply Python to real analytical problems including statistical testing, regression modeling, forecasting, and simulation. You will also learn how to integrate Python outputs into Excel dashboards and reports, creating workflows that are both powerful and practical.Designed for Excel users with no prior programming experience, this book introduces Python concepts gradually and focuses on real-world applications rather than theory. By the end, you will be able to extend Excel in meaningful ways, helping you analyze data more effectively and support better decision-making. What you will learnUse Python directly inside Excel cellsMove data between Excel and DataFramesClean and transform datasets efficientlyBuild advanced visualizations with PythonPerform statistical analysis and modelingForecast trends using time series methodsRun simulations to evaluate uncertaintyCombine Python results with Excel reportsWho this book is forThis book is for business, finance, and operations analysts who rely on Excel and want to go further with their analysis. It is ideal for professionals comfortable with formulas, tables, and PivotTables who want to explore Python without becoming programmers. Early-career data analysts, consultants, and Excel power users will benefit from learning how to extend Excel with more advanced analytical capabilities.
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Data Science with Python
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook DescriptionData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is forData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
R 1,335.00
Hands-On Genetic Algorithms with Python
Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python librariesKey FeaturesLearn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPyTake advantage of cloud computing technology to increase the performance of your solutionsDiscover bio-inspired algorithms such as particle swarm optimization (PSO) and NEATPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms.After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications.By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learnUse genetic algorithms to solve planning, scheduling, gaming, and analytics problemsCreate reinforcement learning, NLP, and explainable AI applicationsEnhance the performance of ML models and optimize deep learning architectureDeploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiencyExplore how images can be reconstructed using a set of semi-transparent shapesDelve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversityWho this book is forIf you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.
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Building Applications with iBeacon
High-precision location information is increasingly useful for mobile application developers, since it allows devices to interact with the world around them. This practical book shows you how to achieve arm's reach accuracy with iBeacons, simple transmitters that enable your applications to react to nearby surroundings and then deliver timely, relevant information - especially indoors, where GPS and cell service are inaccurate. Whether you're enabling a map, giving users directions, creating a game, recommending purchases, letting users check in, or creating an immersive experience, you'll learn how iBeacons provide precise location information, empowering your applications to engage and interact with users nearby.Get examples of several application types you can build with iBeacons Learn how iBeacons provide applications with proximity information Set up, activate, and test iBeacons on both specialized and general-purpose hardware Explore the APIs and tools you need to develop location-aware mobile applications Use built-in iOS features to interact with iBeacons, including Passbook Build networks to help shoppers, travelers, conference attendees, and others find what they're looking for
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Data Science Projects with Python
Gain hands-on experience of Python programming with industry-standard machine learning techniques using pandas, scikit-learn, and XGBoostKey FeaturesThink critically about data and use it to form and test a hypothesisChoose an appropriate machine learning model and train it on your dataCommunicate data-driven insights with confidence and clarityBook DescriptionIf data is the new oil, then machine learning is the drill. As companies gain access to ever-increasing quantities of raw data, the ability to deliver state-of-the-art predictive models that support business decision-making becomes more and more valuable.In this book, you’ll work on an end-to-end project based around a realistic data set and split up into bite-sized practical exercises. This creates a case-study approach that simulates the working conditions you’ll experience in real-world data science projects.You’ll learn how to use key Python packages, including pandas, Matplotlib, and scikit-learn, and master the process of data exploration and data processing, before moving on to fitting, evaluating, and tuning algorithms such as regularized logistic regression and random forest.Now in its second edition, this book will take you through the end-to-end process of exploring data and delivering machine learning models. Updated for 2021, this edition includes brand new content on XGBoost, SHAP values, algorithmic fairness, and the ethical concerns of deploying a model in the real world.By the end of this data science book, you’ll have the skills, understanding, and confidence to build your own machine learning models and gain insights from real data.What you will learnLoad, explore, and process data using the pandas Python packageUse Matplotlib to create compelling data visualizationsImplement predictive machine learning models with scikit-learnUse lasso and ridge regression to reduce model overfittingEvaluate random forest and logistic regression model performanceDeliver business insights by presenting clear, convincing conclusionsWho this book is forData Science Projects with Python – Second Edition is for anyone who wants to get started with data science and machine learning. If you’re keen to advance your career by using data analysis and predictive modeling to generate business insights, then this book is the perfect place to begin. To quickly grasp the concepts covered, it is recommended that you have basic experience of programming with Python or another similar language, and a general interest in statistics.
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Building Low Latency Applications with C++
Understand the impact of reducing latencies on latency sensitive applications.Key FeaturesUnderstand the impact application performance latencies have on different business use cases.Develop deep understanding of C++ features for low latency applications with examples and performance data.Learn to build all the components of a C++ electronic trading system from scratch.Book DescriptionC++ is a general-purpose programming language designed with the goals of efficiency, performance, and flexibility in mind. Nevertheless, real-time low latency applications have very specific requirements in terms of performance latencies.In this book, you will learn about the performance requirements for low latency applications, the C++ features that are critical to achieve the required performance latencies, and learn to build a low latency system in C++ from scratch to solidify your understanding of the C++ principles and techniques involved.You will understand the similarities between such applications, understand the business impact that performance latencies have, and why so much effort is invested to minimize latencies. We will build an entire electronic trading system - matching engine, market data handlers, order gateways and trading algorithms in C++ to teach you the step-by-step low latency development journey from scratch. Additionally, measuring performance and optimizing the critical path is a necessary and continuous process, so we will measure and optimize the performance of our trading system that we build.By the end of this book, you will have a comprehensive understanding of how to design and build low latency applications in C++ from scratch and minimize performance latencies.What you will learnLearn the nature of low latency applications in different businesses.Understand design and implementation of low latency applications.Explore C++ design paradigms and features for low latency development.Explore C++ features that are best avoided in low latency development.Build a complete C++ electronic trading ecosystem from scratch.Learn to measure performance and improve the trading system latencies.Who this book is forThis book is for C++ developers who wish to learn about low latency applications and how to design and develop them effectively. C++ Software engineers looking to apply their knowledge to low latency trading systems like HFT can also learn about which specific C++ features matter and which ones to avoid. Quantitative researchers in the trading industry who wish to learn more about the low latency implementation details can also benefit from this book. The basic requirements for this book are - familiarity with Linux and familiarity with the C++ programming language.
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Django: Web Development with Python
From an idea to a prototype - a complete guide for web development with the Django frameworkAbout This Book* Explore the best practices to develop applications of a superior quality with Django framework* Unravel the common problems of web development in Django* This course teaches you major Django functions and will help you improve your skills by developing models, forms, views, and templates* Experience the challenges of working on an end-to-end social network projectWho This Book Is ForWeb developers who want to use modern Python-based web frameworks like Django to build powerful web applications. The course is mostly self-contained and introduces web development with Python to a reader who is familiar with web development concepts and can help him become an expert in this trade. It's intended for all levels of web developers, both students and practitioners from novice to experts.What You Will Learn* Use Django models to store information in the database and generate queries to access a database across models* Quickly develop web pages to create, read, update, and delete data from the model using class-based views* Generate very maintainable forms with Django* Import data from local sources and external web services as well as exporting your data to third parties* Deep dive into various aspects of Django from models and views to testing and deployment* Familiarize yourself with the various nuances of web development such as browser attacks and databasesIn DetailData science is hot right now, and the need for multitalented developers is greater than ever before. A basic grounding in building apps with a framework as minimalistic, powerful, and easy-to-learn as Django will be a useful skill to launch your career as an entrepreneur or web developer. Django is a web framework that was designed to strike a balance between rapid web development and high performance. This course will take you on a journey to become an efficient web developer thoroughly understanding the key concepts of Django framework. This learning path is divided into three modules. The course begins with basic concepts of the Django framework. The first module, Django Essentials, is like a practical guide, filled with many real-world examples to build highly effective Django web application. After getting familiar with core concepts of Django, it's time to practice your learning from the first module with the help of over 90 recipes available in this module. In the second module, Web Development with Django Cookbook, you'll learn varying complexities to help you create multilingual, responsive, and scalable websites with Django. By the end of this module, you will have a good understanding of the new features added to Django 1.8 and be an expert at web development processes.The next step is to discover the latest best practices and idioms in this rapidly evolving Django framework. This is what you'll be learning in our third module, Django Design Patterns and Best Pra
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Hands-On Serverless Applications with Kotlin
Choose the right architecture and design it using design patterns to create a serverless application that cuts costs and is easily scalableKey FeaturesDesign enterprise ready serverless applications that effortlessly meet your customers’ requirementsEffectively deploy, manage, monitor, and orchestrate serverless applications using AWSUse Cloud9 to provision a secured development environment in the cloudBook DescriptionServerless is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers. Many companies have started using serverless architectures to cut costs and improve scalability. Hands-On Serverless Applications with Kotlin is your one-stop guide to designing serverless architectures for your applications with AWS and Kotlin.To start with, you’ll explore the fundamentals of serverless architecture and how AWS Lambda functions work. You will then learn to design, build, secure, and deploy your application to production. In addition to these activities, you’ll understand how to implement non-functional requirements such as auditing and logging. Moving on, you’ll discover how to scale up and orchestrate serverless applications using an open source framework and handle distributed serverless systems in production. By the end of the book, you’ll have gained the knowledge needed to build scalable and cost-efficient Kotlin applications with a serverless framework.What you will learnDesign a serverless architectureUse AWS Lambda to contain your serverless APIExplore the various ways to keep serverless apps safe and secureUnderstand how a serverless API allows you to use huge infrastructure and cut costsDiscover how to handle distributed systems in KotlinDesign the data flow between cloud services and custom business logicSecure your Kotlin AWS serverless applicationMaster Kotlin design patterns for serverless applicationsWho this book is forHands-On Serverless Applications with Kotlin is for you if you are a Kotlin developer who wants to learn about serverless architectures. It is assumed that you have some knowledge of Kotlin programming and AWS.
R 1,661.00
Deep Learning Research Applications for Natural Language Processing
Humans have the most advanced method of communication, which is known as natural language. While humans can use computers to send voice and text messages to each other, computers do not innately know how to process natural language. In recent years, deep learning has primarily transformed the perspectives of a variety of fields in artificial intelligence (AI), including speech, vision, and natural language processing (NLP). The extensive success of deep learning in a wide variety of applications has served as a benchmark for the many downstream tasks in AI. The field of computer vision has taken great leaps in recent years and surpassed humans in tasks related to detecting and labeling objects thanks to advances in deep learning and neural networks. Deep Learning Research Applications for Natural Language Processing explains the concepts and state-of-the-art research in the fields of NLP, speech, and computer vision. It provides insights into using the tools and libraries in Python for real-world applications. Covering topics such as deep learning algorithms, neural networks, and advanced prediction, this premier reference source is an excellent resource for computational linguists, software engineers, IT managers, computer scientists, students and faculty of higher education, libraries, researchers, and academicians.
R 11,359.26
Programming Social Applications
Like all good web ventures, the Social Web has risen in prominence ahead of any reliable standards that would ensure a consistent user experience. But that's finally beginning to change. With this book, you'll learn about a rich set of open source technologies for building compelling-and secure-social applications and websites. You'll discover what it takes to engage users on a personal level, and learn ways to monetize your venture. Programming Social Applications introduces you to the open source tools used on many major social platforms, and explains how technologies such as OpenSocial, Apache Shindig, OAuth, OpenID, Caja, and others work together to help you solve practical issues. You'll also examine successful proprietary platforms to help you gain a comprehensive understanding of the Social Web's foundations.* Learn how to build viral applications with social data * Implement social web technologies using real-world examples * Work with OpenSocial to standardize social graphs-individual nodes and their relationships * Build a scalable OpenSocial application container with Apache Shindig * Develop comprehensive user authentication and authorization systems using OAuth and OpenID * Learn how Caja, and ADsafe provide user security on social websites * Deal with bugs and other problems when implementing these technologies * Build distributed web frameworks that extend social graphs to include any social footprints users leave on the Web
R 1,026.00
Dynamical Systems with Applications using Maple™
Excellent reviews of the first edition (Mathematical Reviews, SIAM, Reviews, UK Nonlinear News, The Maple Reporter)New edition has been thoroughly updated and expanded to include more applications, examples, and exercises, all with solutionsTwo new chapters on neural networks and simulation have also been addedWide variety of topics covered with applications to many fields, including mechanical systems, chemical kinetics, economics, population dynamics, nonlinear optics, and materials scienceAccessible to a broad, interdisciplinary audience of readers with a general mathematical background, including senior undergraduates, graduate students, and working scientists in various branches of applied mathematics, the natural sciences, and engineeringA hands-on approach is used with Maple as a pedagogical tool throughout; Maple worksheet files are listed at the end of each chapter, and along with commands, programs, and output may be viewed in color at the author’s website with additional applications and further links of interest at Maplesoft’s Application Center
R 2,536.14
Calculus With Applications
Burstein, and Lax's Calculus with Applications and Computing offers meaningful explanations of the important theorems of single variable calculus. Written with students in mathematics, the physical sciences, and engineering in mind, and revised with their help, it shows that the themes of calculation, approximation, and modeling are central to mathematics and the main ideas of single variable calculus. This edition brings the innovation of the first edition to a new generation of students. New sections in this book use simple, elementary examples to show that when applying calculus concepts to approximations of functions, uniform convergence is more natural and easier to use than point-wise convergence. As in the original, this edition includes material that is essential for students in science and engineering, including an elementary introduction to complex numbers and complex-valued functions, applications of calculus to modeling vibrations and population dynamics, and an introduction to probability and information theory.
R 2,398.35
Data Science and Analytics with Python
Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI).This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts.Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.
R 3,171.08
Python 3 Text Processing with NLTK 3 Cookbook
This book is intended for Python programmers interested in learning how to do natural language processing. Maybe you've learned the limits of regular expressions the hard way, or you've realized that human language cannot be deterministically parsed like a computer language. Perhaps you have more text than you know what to do with, and need automated ways to analyze and structure that text. This Cookbook will show you how to train and use statistical language models to process text in ways that are practically impossible with standard programming tools. A basic knowledge of Python and the basic text processing concepts is expected. Some experience with regular expressions will also be helpful.
R 1,661.00
Web Applications with Elm
Learn the basics of the Elm platform for web applications. This book covers the language as of version 0.18 and the most important libraries. After reading this book you will have an understanding what Elm can do for you. Also, you will be able to build on the example in the book to develop advanced web applications with Elm.What You'll Learn:Work with Elm and its development environmentLearn the language and libraries in examplesUse the Elm architecture to create applications with the Elm platformPut it all together with a sample application and explanation that covers the implementation detailsWho This Book Is For:Web developers new to Elm, with some experience in JavaScript recommended. This book is also for others curious about Elm and its potential beyond web development.
R 880.13
Python for Microcontrollers: Getting Started with MicroPython
Build and program your own electronics projects with MicroPython in no time!This practical guide offers a hands-on introduction to Python-based microcontroller programming with MicroPython and the open-source Pyboard hardware platform. The book shows, step-by-step, how to set up and interface with the board, install the necessary software, and develop custom MicroPython programs.Written by an experienced hobbyist, Python for Microcontrollers: Getting Started with MicroPython and Pyboard features start-to-finish, DIY projects that clearly demonstrate each technique. You will learn how to use the built-in sensor, store data to an SD card, control the LCD and matrix keyboard, interface with the Web—even build a cool robotic car! From there, you will discover how to assemble, program, and troubleshoot all kinds of entertaining and practical projects of your own. • Serves both as a hobbyists’ guide and as an introduction for engineers and techs • Features clear explanations, well-illustrated examples, and hands-on projects• Written in clear, easy-to-follow language by an experienced maker
R 724.41
R 855.96
Building Applications with Azure Resource Manager (ARM)
Learn how to leverage infrastructure as code with Azure Resource Manager (ARM) and the best practices to build, test, debug, and deploy your applications in Microsoft Azure using ARM templates and the new domain-specific language Bicep.As organizations consider moving partially or fully to the cloud, infrastructure as code (IaC) has become a key component to improving time to market for their applications. However, it is critical that enterprise infrastructure professionals use the right resources and strategies to build the infrastructure required to run applications in the cloud. Azure cloud-native components and capabilities automate the build and deployment process, offering a myriad of compelling reasons to leverage IaC to build your applications in Azure.Starting with the evolution of the software-defined approach and building on the basic concepts of infrastructure as code, this book provides you with comprehensive guidance to learn Azure Resource Manager from the ground up. You will learn the best practices for deploying and maintaining application infrastructure, such as template authoring tooling enhancements, Azure DevOps integrations, and updates to the deployment platform.After reading this book, you will understand the breadth and use cases of ARM capabilities and tooling within Microsoft Azure. You will be able to build, test, debug, and deploy your ARM templates and know how to use infrastructure as code to better manage the life cycle of your applications in Azure.What You Will LearnIntroduces the concepts of infrastructure as code and how to leverage it using Azure Resource ManagerTeaches how to enable and deploy Azure Resource Manager templatesCovers the basic core elements of an ARM template to start authoring your templates and building your applications in the cloudDives into core components such as parameters, variables, functions, dependencies, deployment modes, loops, conditions), enabling nested templates Reveals the built-in services and features in Azure, allowing the debugging process and validation process of the ARM templatesDiscusses “Bicep” the language for ARM templates and the DevOps integration to deploy ARM templatesWho This Book Is ForPeople who build, install, repair, or maintain the hardware and software associated with computer systems on-premises and who want to learn about the deployment and provisioning process of applications in the Microsoft Cloud using an infrastructure as code approach through Azure Resource Manager. Readers should have an Azure account and be familiar with the main Azure services (storage, compute, networking, management). Basic knowledge of PowerShell is also useful.
R 2,420.52
Network Science with Python
Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in colorKey FeaturesCreate networks using data points and informationLearn to visualize and analyze networks to better understand communitiesExplore the use of network data in both - supervised and unsupervised machine learning projectsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNetwork analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You'll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You'll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you'll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You'll also explore network analysis concepts, from basics to an advanced level.By the end of the book, you'll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.What you will learnExplore NLP, network science, and social network analysisApply the tech stack used for NLP, network science, and analysisExtract insights from NLP and network dataGenerate personalized NLP and network projectsAuthenticate and scrape tweets, connections, the web, and data streamsDiscover the use of network data in machine learning projectsWho this book is forNetwork Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.
R 1,693.00
Hands-on Machine Learning with Python
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoretical and practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage. What You'll LearnReview data structures in NumPy and Pandas Demonstrate machine learning techniques and algorithmUnderstand supervised learning and unsupervised learning Examine convolutional neural networks and Recurrent neural networksGet acquainted with scikit-learn and PyTorchPredict sequences in recurrent neural networks and long short term memory Who This Book Is ForData scientists, machine learning engineers, and software professionals with basic skills in Python programming.
R 1,482.00
Web Applications with Javascript or Java
Today, web applications are the most important type of software applications. This textbook shows how to design and implement them, using a model-based engineering approach that covers general information management concepts and techniques and the two most relevant technology platforms: JavaScript and Java. The book provides an in-depth tutorial for theory-underpinned and example-based learning by doing it yourself, supported by quiz questions and practice projects. Volume 1 provides an introduction to web technologies and model-based web application engineering, discussing the information management concepts of constraint-based data validation, enumerations and special datatypes. Volume 2 discusses the advanced information management concepts of associations and inheritance in class hierarchies. Web apps are designed using UML class diagrams and implemented with two technologies: JavaScript for front-end (and distributed NodeJS) apps, and Java (with JPA and JSF) for back-end apps. The six example apps discussed in the book can be run, and their source code downloaded, from the book’s website.
R 1,968.77
Hands-On Image Processing with Python
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, we will have learned to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
R 1,661.00
Building Hybrid Android Applications with Java and JavaScript
Build HTML5-based hybrid applications for Android with a mix of native Java and JavaScript components, without using third-party libraries and wrappers such as PhoneGap or Titanium. This concise, hands-on book takes you through the entire process, from setting up your development environment to deploying your product to an app store. Learn how to create apps that have access to native APIs, such as location, vibrator, sensors, and the camera, using a JavaScript/Java bridge - and choose the language that gives you better performance for each task. If you have experience with HTML5 and JavaScript, you'll quickly discover why hybrid app development is the wave of the future. Set up a development environment with HTML, CSS, and JavaScript tools Create your first hybrid Android project, using Eclipse IDE Use the WebView control to host your hybrid application Explore hybrid application architecture, including JavaScript/Java communication Build single-page applications, using JavaScript libraries such as Backbone and Underscore Get optimization tips and useful snippets for CSS, DOM, and JavaScript Distribute your application to Google Play and the Amazon Appstore
R 692.00
