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Building Computer Vision Applications Using Artificial Neural Networks
Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition’s publication. All code used in the book has also been fully updated.This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you’ll gain a thorough understanding of them. The book’s source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you’ll have the knowledge and skills to build your own computer vision applications using neural networks What You Will LearnUnderstand image processing, manipulation techniques, and feature extraction methodsWork with convolutional neural networks (CNN), single-shot detector (SSD), and YOLOUtilize large scale model development and cloud infrastructure deploymentGain an overview of FaceNet neural network architecture and develop a facial recognition systemWho This Book Is ForThose who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students.
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Machine Learning for Biomedical Applications
Machine Learning for Biomedical Applications: With Scikit-Learn and PyTorch presents machine learning techniques most commonly used in a biomedical setting. Avoiding a theoretical perspective, it provides a practical and interactive way of learning where concepts are presented in short descriptions followed by simple examples using biomedical data. Interactive Python notebooks are provided with each chapter to complement the text and aid understanding. Sections cover uses in biomedical applications, practical Python coding skills, mathematical tools that underpin the field, core machine learning methods, deep learning concepts with examples in Keras, and much more. This accessible and interactive introduction to machine learning and data analysis skills is suitable for undergraduates and postgraduates in biomedical engineering, computer science, the biomedical sciences and clinicians.
R 2,206.00
Building Machine Learning Powered Applications
Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you:Define your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML models and address performance bottlenecksDeploy and monitor your models in a production environment
R 1,505.00
Building REST APIs with Flask
Develop RESTful web services using the Flask micro-framework and integrate them using MySQL. Use Flask to develop, deploy, and manage REST APIs with easy-to-read and understand Python code. Solve your problem from a choice of libraries. Learn to use MySQL as the web services database for your Flask API using SQLAlchemy ORM.Building REST APIs with Flask provides a primer on Flask, RESTful services, and working with pip to set up your virtual environment. The key differences between NoSQL and SQL are covered, and you are taught how to connect MySQL and Flask using SQLAlchemy. Author Kunal Relan presents best practices for creating REST APIs and guides you in structuring your app and testing REST endpoints. He teaches you how to set up authentication and render HTML using views. You learn how to write unit tests for your REST APIs, and understand mocks, assertions, and integration testing. You will know how to document your REST APIs, deploy your Flask application on all of the major cloud platforms, and debug and monitor your Flask application.What You'll LearnUse MySQL to create Flask REST APIs Test REST endpointsCreate CRUD endpoints with Flask and MySQLDeploy Flask on all of the major cloud platformsMonitor your Flask application Who This Book Is ForPython developers interested in REST API development using Flask and web developers with basic programming knowledge who want to learn how Python and REST APIs work together. Readers should be familiar with Python (command line, or at least pip) and MySQL.
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Deep Learning with Python
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to productionWho This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
R 1,965.56
Building Python Web APIs with FastAPI
Discover FastAPI features and best practices for building and deploying high-quality web APIs from scratchKey FeaturesA practical guide to developing production-ready web APIs rapidly in PythonLearn how to put FastAPI into practice by implementing it in real-world scenariosExplore FastAPI, its syntax, and configurations for deploying applicationsBook DescriptionRESTful web services are commonly used to create APIs for web-based applications owing to their light weight and high scalability. This book will show you how FastAPI, a high-performance web framework for building RESTful APIs in Python, allows you to build robust web APIs that are simple and intuitive and makes it easy to build quickly with very little boilerplate code. This book will help you set up a FastAPI application in no time and show you how to use FastAPI to build a REST API that receives and responds to user requests. You'll go on to learn how to handle routing and authentication while working with databases in a FastAPI application. The book walks you through the four key areas: building and using routes for create, read, update, and delete (CRUD) operations; connecting the application to SQL and NoSQL databases; securing the application built; and deploying your application locally or to a cloud environment.By the end of this book, you'll have developed a solid understanding of the FastAPI framework and be able to build and deploy robust REST APIs.What you will learnSet up a FastAPI application that is fully functional and secureUnderstand how to handle errors from requests and send proper responses in FastAPIIntegrate and connect your application to a SQL and NoSQL (MongoDB) databasePerform CRUD operations using SQL and FastAPIManage concurrency in FastAPI applicationsImplement authentication in a FastAPI applicationDeploy a FastAPI application to any platformWho this book is forThis book is for Python developers who want to learn FastAPI in a pragmatic way to create robust web APIs with ease. If you are a Django or Flask developer looking to try something new that's faster, more efficient, and produces fewer bugs, this FastAPI Python book is for you. The book assumes intermediate-level knowledge of Python programming.
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Building Versatile Mobile Apps with Python and REST
Develop versatile iOS and Android apps using Python withRESTful web services. Dive into full-stack development with Django, a powerfulPython framework, and React Native, the most in-demand JavaScript library.Begin by building a mobile app using the RESTful APIs andReact Native. Starting from scratch, create a database and serialize the datawith Django REST to serve APIs. Then build the front-end with React and mobileapps for iOS and Android with React Native. By the end of the book, you’ll have developed three appspowered by Django—a desktop React app, an iOS app, and an Android app. Discoverthe whole process of developing apps from inception to distribution of an iOSapp in the Apple store and an Android app in the Google Play store. You will:Develop using the Model-View-Controller patternFacilitate the communications between the back-end andfront-end of web apps with HTTPDesign a robust front-end for an app with ReactCreate one back-end solution for both iOS and Androiddevices with Django
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Practical Computer Vision Applications Using Deep Learning with CNNs
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. What You Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using PythonFollow a deep learning project from conception to production using TensorFlowUse NumPy with Kivy to build cross-platform data science applicationsWho This Book Is ForData scientists, machine learning and deep learning engineers, software developers.
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Federated Learning with Python
Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.What you will learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho this book is forThis book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.
R 1,596.00
Electrodynamics Tutorials with Python Simulations
This book provides an accessible introduction to intermediate-level electrodynamics with computational approaches to complement a traditional mathematical treatment of the subject. It covers key topics in electrodynamics, such as electromagnetic fields, forces, potentials, and waves as well as Special Theory of Relativity.Through intuition-building examples and visualizations in the Python programming language, it helps readers to develop technical computing skills in numerical and symbolic calculations, modeling and simulations, and visualizations. Python is a highly readable and practical programming language, making this book appropriate for students without extensive programming experience.This book can serve as an electrodynamics textbook for undergraduate physics and engineering students in their second or third years, who are studying intermediate- or advanced-level electrodynamics and who want to learn techniques for scientific computing at the same time. This book will also appeal to computer science students who want to see how their computer programming skills may be applied to science, particularly to physics, without needing too much background physics knowledge.Key features● Major concepts in classical electrodynamics are introduced cohesively through computational and mathematical treatments.● Computational examples in Python programming language guide students on how to simulate and visualize electrodynamic principles and phenomena for themselves.Taejoon Kouh is a Professor of Nano and Electronic Physics at Kookmin University, Republic of Korea. He earned his B.A. in physics from Boston University and Sc.M. and Ph.D. degrees in physics from Brown University. After his study in Providence, RI, he returned to Boston, MA, and worked as a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at Boston University. He is a full faculty member in the Department of Nano and Electronic Physics at Kookmin University in Seoul, Korea, teaching and supervising undergraduate and graduate students. His current research involves the dynamics of nanoelectromechanical systems and the development of fast and reliable transduction methods and innovative applications based on tiny motion.Minjoon Kouh is a program scientist for a philanthropic initiative. He was a Professor of Physics and Neuroscience at Drew University, USA, where he taught more than 30 distinct types of courses. He holds Ph.D. and B.S. degrees in physics from MIT and an M.A. from UC Berkeley. He completed a postdoctoral research fellowship at the Salk Institute for Biological Studies in La Jolla, CA. His research includes computational modeling of the primate visual cortex, information-theoretic analysis of neural responses, machine learning, and pedagogical innovations in undergraduate science education.
R 7,580.14
Hands-On Ensemble Learning with Python
Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.What you will learnImplement ensemble methods to generate models with high accuracyOvercome challenges such as bias and varianceExplore machine learning algorithms to evaluate model performanceUnderstand how to construct, evaluate, and apply ensemble modelsAnalyze tweets in real time using Twitter's streaming APIUse Keras to build an ensemble of neural networks for the MovieLens datasetWho this book is forThis book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.
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Deep Learning with Python
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group.You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.What You'll LearnReview machine learning fundamentals such as overfitting, underfitting, and regularization.Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.Apply in-depth linear algebra with PyTorchExplore PyTorch fundamentals and its building blocksWork with tuning and optimizing models Who This Book Is ForBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
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Modern Data Architectures with Python
Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and KafkaKey FeaturesDevelop modern data skills used in emerging technologiesLearn pragmatic design methodologies such as Data Mesh and data lakehousesGain a deeper understanding of data governancePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionModern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake.Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market.By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What you will learnUnderstand data patterns including delta architectureDiscover how to increase performance with Spark internalsFind out how to design critical data diagramsExplore MLOps with tools such as AutoML and MLflowGet to grips with building data products in a data meshDiscover data governance and build confidence in your dataIntroduce data visualizations and dashboards into your data practiceWho this book is forThis book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.
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Crafting Test-Driven Software with Python
Get to grips with essential concepts and step-by-step explanations to apply TDD practices to your Python projects while keeping your test suite under controlKey FeaturesBuild robust Python applications using TDD and BDD methodologiesTest Python web applications using WebTest and web frameworksLeverage PyTest to implement stringent testing mechanisms to ensure fault-tolerant applicationsBook DescriptionTest-driven development (TDD) is a set of best practices that helps developers to build more scalable software and is used to increase the robustness of software by using automatic tests. This book shows you how to apply TDD practices effectively in Python projects. You’ll begin by learning about built-in unit tests and Mocks before covering rich frameworks like PyTest and web-based libraries such as WebTest and Robot Framework, discovering how Python allows you to embrace all modern testing practices with ease. Moving on, you’ll find out how to design tests and balance them with new feature development and learn how to create a complete test suite with PyTest. The book helps you adopt a hands-on approach to implementing TDD and associated methodologies that will have you up and running and make you more productive in no time. With the help of step-by-step explanations of essential concepts and practical examples, you’ll explore automatic tests and TDD best practices and get to grips with the methodologies and tools available in Python for creating effective and robust applications. By the end of this Python book, you will be able to write reliable test suites in Python to ensure the long-term resilience of your application using the range of libraries offered by Python for testing and development.What you will learnFind out how tests can make your life easier as a developer and discover related best practicesExplore PyTest, the most widespread testing framework for PythonGet to grips with the most common PyTest plugins, including coverage, flaky, xdist, and pickedWrite functional tests for WSGI web applications with WebTestRun end-to-end tests for web applications using Robot FrameworkUnderstand what test-driven development means and why it is importantDiscover how to use the range of tools available in PythonBuild reliable and robust applicationsWho this book is forThis book is for Python developers looking to get started with test-driven development and developers who want to learn about the testing tools available in Python. Developers who want to create web applications with Python and plan to implement TDD methodology with PyTest will find this book useful. Basic knowledge of Python programming is required.
R 1,661.00
Learning Geospatial Analysis with Python -
An effective guide to geographic information systems and remote sensing analysis using Python 3About This Book• Construct applications for GIS development by exploiting Python• This focuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system—no compiling of C libraries necessary• This practical, hands-on tutorial teaches you all about Geospatial analysis in Python Who This Book Is ForIf you are a Python developer, researcher, or analyst who wants to perform Geospatial, modeling, and GIS analysis with Python, then this book is for you. Familarity with digital mapping and analysis using Python or another scripting language for automation or crunching data manually is appreciatedWhat You Will Learn• Automate Geospatial analysis workflows using Python• Code the simplest possible GIS in 60 lines of Python• Mold thematic maps with Python tools• Get hold of the various forms that geospatial data comes in• Produce elevation contours using Python tools• Create flood inundation models• Apply Geospatial analysis to find out about real-time data tracking and for storm chasingIn DetailGeospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software.Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data.This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.Style and approach This is a practical, hands-on tutorial that teaches you all about Geospatial analysis interactively using Python.
R 1,889.00
Supercharge Your Applications with GraalVM
Understand the internals and architecture of GraalVM with the help of hands-on experiments and gain deep knowledge that you can apply to improve your application's performance, interoperability, and throughput.Key FeaturesGenerate faster and leaner code with minimum computing resources for high performanceCompile Java applications faster than ever to a standalone executable called native imagesCreate high-performance polyglot applications that are compatible across various JVM and non-JVM languagesBook DescriptionGraalVM is a universal virtual machine that allows programmers to compile and run applications written in both JVM and non-JVM languages. It improves the performance and efficiency of applications, making it an ideal companion for cloud-native or microservices-based applications. This book is a hands-on guide, with step-by-step instructions on how to work with GraalVM. Starting with a quick introduction to the GraalVM architecture and how things work under the hood, you'll discover the performance benefits of running your Java applications on GraalVM. You'll then learn how to create native images and understand how AOT (ahead-of-time) can improve application performance significantly. The book covers examples of building polyglot applications that will help you explore the interoperability between languages running on the same VM. You'll also see how you can use the Truffle framework to implement any language of your choice to run optimally on GraalVM. By the end of this book, you'll not only have learned how GraalVM is beneficial in cloud-native and microservices development but also how to leverage its capabilities to create high-performing polyglot applications.What you will learnGain a solid understanding of GraalVM and how it works under the hoodWork with GraalVM's high performance optimizing compiler and see how it can be used in both JIT (just-in-time) and AOT (ahead-of-time) modesGet to grips with the various optimizations that GraalVM performs at runtimeUse advanced tools to analyze and diagnose performance issues in the codeCompile, embed, run, and interoperate between languages using Truffle on GraalVMBuild optimum microservices using popular frameworks such as Micronaut and Quarkus to create cloud-native applicationsWho this book is forThis book is for JVM developers looking to optimize their application's performance. You'll also find this book useful if you're a JVM developer looking to explore options to develop polyglot applications using tools from the Python, R, Ruby, or Node.js ecosystem. A solid understanding of software development concepts and prior experience working with programming languages is necessary to get started.
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Hands-On Unsupervised Learning with Python
Discover the skill-sets required to implement various approaches to Machine Learning with PythonKey FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook DescriptionUnsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.What you will learnUse cluster algorithms to identify and optimize natural groups of dataExplore advanced non-linear and hierarchical clustering in actionSoft label assignments for fuzzy c-means and Gaussian mixture modelsDetect anomalies through density estimationPerform principal component analysis using neural network modelsCreate unsupervised models using GANsWho this book is forThis book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
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Supervised Learning with Python
Gain a thorough understanding of supervised learning algorithms by developing use cases with Python. You will study supervised learning concepts, Python code, datasets, best practices, resolution of common issues and pitfalls, and practical knowledge of implementing algorithms for structured as well as text and images datasets.You’ll start with an introduction to machine learning, highlighting the differences between supervised, semi-supervised and unsupervised learning. In the following chapters you’ll study regression and classification problems, mathematics behind them, algorithms like Linear Regression, Logistic Regression, Decision Tree, KNN, Naïve Bayes, and advanced algorithms like Random Forest, SVM, Gradient Boosting and Neural Networks. Python implementation is provided for all the algorithms. You’ll conclude with an end-to-end model development process including deployment and maintenance of the model.After reading Supervised Learning with Python you’ll have a broad understanding of supervised learning and its practical implementation, and be able to run the code and extend it in an innovative manner.What You'll LearnReview the fundamental building blocks and concepts of supervised learning using PythonDevelop supervised learning solutions for structured data as well as text and images Solve issues around overfitting, feature engineering, data cleansing, and cross-validation for building best fit modelsUnderstand the end-to-end model cycle from business problem definition to model deployment and model maintenance Avoid the common pitfalls and adhere to best practices while creating a supervised learning model using PythonWho This Book Is ForData scientists or data analysts interested in best practices and standards for supervised learning, and using classification algorithms and regression techniques to develop predictive models.
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Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science
The world is approaching a point where big data will start to play a beneficial role in many industries and organizations. Today, analyzing data for new insights has become an everyday norm, increasing the need for data analysts to use efficient and appropriate tools to provide quick and valuable results to clients. Existing research in the field currently lacks a full coverage of all essential algorithms, leaving a knowledge void for practical implementation and code in Python with all needed libraries and links to datasets used. Advanced Interdisciplinary Applications of Machine Learning Python Libraries for Data Science serves as a one-stop book to help emerging data scientists gain hands-on skills needed through real-world data and completely up-to-date Python code. It covers all the technical details, from installing the needed software to importing libraries and using the latest data sets; deciding on the right model; training, testing, and evaluating the model; and including NumPy, Pandas, and matplotlib. With coverage on various machine learning algorithms like regression, linear and logical regression, classification, support vector machine (SVM), clustering, k-nearest neighbor, market basket analysis, Apriori, k-means clustering, and visualization using Seaborne, it is designed for academic researchers, undergraduate students, postgraduate students, executive education program leaders, and practitioners.
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Metaprogramming with Python
A practical approach to metaprogramming with real-world examples that enables the development of advanced frameworks, libraries, and applications using PythonKey FeaturesLearn applied metaprogramming through a simple step-by-step approachWork with easily understandable examples and explanations that take you deep into the theory of metaprogrammingGet practical experience in writing reusable code with real-world examplesBook DescriptionEffective and reusable code makes your application development process seamless and easily maintainable. With Python, you will have access to advanced metaprogramming features that you can use to build high-performing applications. The book starts by introducing you to the need and applications of metaprogramming, before navigating the fundamentals of object-oriented programming. Next, you will learn about simple decorators, work with metaclasses, and later focus on introspection and reflection. You'll also delve into generics and typing before defining templates for algorithms. As you progress, you will understand your code using abstract syntax trees and explore method resolution order. This Python book also shows you how to create your own dynamic objects before structuring the objects through design patterns. Finally, you will learn simple code-generation techniques along with discovering best practices and eventually building your own applications. By the end of this learning journey, you'll have acquired the skills and confidence you need to design and build reusable high-performing applications that can solve real-world problems.What you will learnUnderstand the programming paradigm of metaprogramming and its needRevisit the fundamentals of object-oriented programmingDefine decorators and work with metaclassesEmploy introspection and reflection on your codeApply generics, typing, and templates to enhance your codeGet to grips with the structure of your code through abstract syntax trees and the behavior through method resolution orderCreate dynamic objects and generate dynamic codeUnderstand various design patterns and best practicesWho this book is forIf you are an intermediate-level Python programmer looking to enhance your coding skills by developing reusable and advanced frameworks, then this book is for you. Basic knowledge of Python programming will help you get the most out of this learning journey.
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Introduction to Scientific Programming with Python
This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.
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Practical Natural Language Processing with Python
Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python. Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling. By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book.What You Will LearnBuild an understanding of NLP problems in industryGain the know-how to solve a typical NLP problem using language-based models and machine learningDiscover the best methods to solve a business problem using NLP - the tried and tested onesUnderstand the business problems that are tough to solve Who This Book Is ForAnalytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve the problems at hand.
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Learn Web Development with Python
A comprehensive guide to Python programming for web development using the most popular Python web framework - DjangoKey FeaturesLearn the fundamentals of programming with Python and building web appsBuild web applications from scratch with DjangoCreate real-world RESTful web services with the latest Django frameworkBook DescriptionIf you want to develop complete Python web apps with Django, this Learning Path is for you. It will walk you through Python programming techniques and guide you in implementing them when creating 4 professional Django projects, teaching you how to solve common problems and develop RESTful web services with Django and Python. You will learn how to build a blog application, a social image bookmarking website, an online shop, and an e-learning platform.Learn Web Development with Python will get you started with Python programming techniques, show you how to enhance your applications with AJAX, create RESTful APIs, and set up a production environment for your Django projects. Last but not least, you’ll learn the best practices for creating real-world applications.By the end of this Learning Path, you will have a full understanding of how Django works and how to use it to build web applications from scratch.This Learning Path includes content from the following Packt products:Learn Python Programming by Fabrizio RomanoDjango RESTful Web Services by Gastón C. HillarDjango Design Patterns and Best Practices by Arun RavindranWhat you will learnExplore the fundamentals of Python programming with interactive projectsGrasp essential coding concepts along with the basics of data structures and control flowDevelop RESTful APIs from scratch with Django and the Django REST FrameworkCreate automated tests for RESTful web servicesDebug, test, and profile RESTful web services with Django and the Django REST FrameworkUse Django with other technologies such as Redis and CeleryWho this book is forIf you have little experience in coding or Python and want to learn how to build full-fledged web apps, this Learning Path is for you. No prior experience with RESTful web services, Python, or Django is required, but basic Python programming experience is needed to understand the concepts covered.
R 1,628.00
Building Search Applications: Lucene, LingPipe, and Gate
Lucene, LingPipe, and Gate are popular open source tools to build powerful search applications. Building Search Applications describes functions from Lucene that include indexing, searching, ranking, and spelling correction to build search engines. With this book you will learn to: Extract tokens from text using custom tokenizers and analyzers from Lucene, LingPipe, and Gate. Construct a search engine index with an optional backend database to manage large document collections. Explore the wide range of Lucene queries to search an index, understand the ranking algorithm for a query, and suggest spelling corrections. Find the names of people, places, and other entities in text using LingPipe and Gate. Categorize documents by topic using classifiers and build groups of self-organized documents using clustering algorithms from LingPipe. Create a Web crawler to scan the Web, Intranet, or desktop using Nutch. Track the sentiment of articles published on the Web with LingPipe.
R 1,262.00
Advanced Deep Learning with Python
Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystemKey FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook DescriptionIn order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is forThis book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
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Python GUI Programming with Tkinter
Transform your evolving user requirements into feature-rich Tkinter applicationsKey FeaturesExtensively revised with new content on RESTful networking, classes in Tkinter, and the Notebook widgetTake advantage of Tkinter’s lightweight, portable, and easy-to-use featuresBuild better-organized code and learn to manage an evolving codebaseBook DescriptionTkinter is widely used to build GUIs in Python due to its simplicity. In this book, you’ll discover Tkinter’s strengths and overcome its challenges as you learn to develop fully featured GUI applications.Python GUI Programming with Tkinter, Second Edition, will not only provide you with a working knowledge of the Tkinter GUI library, but also a valuable set of skills that will enable you to plan, implement, and maintain larger applications. You’ll build a full-blown data entry application from scratch, learning how to grow and improve your code in response to continually changing user and business needs.You’ll develop a practical understanding of tools and techniques used to manage this evolving codebase and go beyond the default Tkinter widget capabilities. You’ll implement version control and unit testing, separation of concerns through the MVC design pattern, and object-oriented programming to organize your code more cleanly.You’ll also gain experience with technologies often used in workplace applications, such as SQL databases, network services, and data visualization libraries. Finally, you’ll package your application for wider distribution and tackle the challenge of maintaining cross-platform compatibility.What you will learnProduce well-organized, functional, and responsive GUI applicationsExtend the functionality of existing widgets using classes and OOPPlan wisely for the expansion of your app using MVC and version controlMake sure your app works as intended through widget validation and unit testingUse tools and processes to analyze and respond to user requestsBecome familiar with technologies used in workplace applications, including SQL, HTTP, Matplotlib, threading, and CSVUse PostgreSQL authentication to ensure data security for your applicationWho this book is forThis book is for programmers who understand the syntax of Python, but do not yet have the skills, techniques, and knowledge to design and implement a complete software application. A fair grasp of basic Python syntax is required.
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Hands-On Simulation Modeling with Python
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with easeKey FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook DescriptionSimulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is forThis book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.
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Building Parallel, Embedded, and Real-Time Applications with Ada
The arrival and popularity of multi-core processors has sparked a renewed interest in the development of parallel programs. Similarly, the availability of low-cost microprocessors and sensors has generated a great interest in embedded real-time programs. This book provides students and programmers whose backgrounds are in traditional sequential programming with the opportunity to expand their capabilities into parallel, embedded, real-time and distributed computing. It also addresses the theoretical foundation of real-time scheduling analysis, focusing on theory that is useful for actual applications. Written by award-winning educators at a level suitable for undergraduates and beginning graduate students, this book is the first truly entry-level textbook in the subject. Complete examples allow readers to understand the context in which a new concept is used, and enable them to build and run the examples, make changes, and observe the results.
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R 611.00
Hands-On Reactive Programming with Python
A comprehensive guide to help you understand the principles of Reactive and asynchronous programming and its benefitsKey FeaturesExplore the advantages of Reactive programmingUse concurrency and parallelism in RxPY to build powerful reactive applicationsDeploy and scale your reactive applications using DockerBook DescriptionReactive programming is central to many concurrent systems, but it’s famous for its steep learning curve, which makes most developers feel like they're hitting a wall. With this book, you will get to grips with reactive programming by steadily exploring various conceptsThis hands-on guide gets you started with Reactive Programming (RP) in Python. You will learn abouta the principles and benefits of using RP, which can be leveraged to build powerful concurrent applications. As you progress through the chapters, you will be introduced to the paradigm of Functional and Reactive Programming (FaRP), observables and observers, and concurrency and parallelism. The book will then take you through the implementation of an audio transcoding server and introduce you to a library that helps in the writing of FaRP code. You will understand how to use third-party services and dynamically reconfigure an application.By the end of the book, you will also have learned how to deploy and scale your applications with Docker and Traefik and explore the significant potential behind the reactive streams concept, and you'll have got to grips with a comprehensive set of best practices.What you will learnStructure Python code for better readability, testing, and performanceExplore the world of event-based programmingGrasp the use of the most common operators in RxUnderstand reactive extensions beyond simple examplesMaster the art of writing reusable componentsDeploy an application on a cloud platform with Docker and TraefikWho this book is forIf you are a Python developer who wants to learn Reactive programming to build powerful concurrent and asynchronous applications, this book is for you. Basic understanding of the Python language is all you need to understand the concepts covered in this book.
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Machine Learning Theory and Applications
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much moreClassical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs)Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related dataFeature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applicationsMachine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
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Simulation with Python
Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python.What You'll LearnUse Python and numerical computation to demonstrate the power of simulationChoose a paradigm to run a simulationDraw statistical insights from numerical experimentsKnow how simulation is used to solve real-world problems Who This Book Is ForEntry-level to mid-level Python developers from various backgrounds, including backend developers, academic research programmers, data scientists, and machine learning engineers. The book is also useful to high school students and college undergraduates and graduates with STEM backgrounds.
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Applied Computational Thinking with Python
Use the computational thinking philosophy to solve complex problems by designing appropriate algorithms to produce optimal results across various domainsKey FeaturesDevelop logical reasoning and problem-solving skills that will help you tackle complex problemsExplore core computer science concepts and important computational thinking elements using practical examplesFind out how to identify the best-suited algorithmic solution for your problemBook DescriptionComputational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.What you will learnFind out how to use decomposition to solve problems through visual representationEmploy pattern generalization and abstraction to design solutionsBuild analytical skills required to assess algorithmic solutionsUse computational thinking with Python for statistical analysisUnderstand the input and output needs for designing algorithmic solutionsUse computational thinking to solve data processing problemsIdentify errors in logical processing to refine your solution designApply computational thinking in various domains, such as cryptography, economics, and machine learningWho this book is forThis book is for students, developers, and professionals looking to develop problem-solving skills and tactics involved in writing or debugging software programs and applications. Familiarity with Python programming is required.
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Hyperparameter Tuning with Python
Take your machine learning models to the next level by learning how to leverage hyperparameter tuning, allowing you to control the model’s finest detailsKey FeaturesGain a deep understanding of how hyperparameter tuning worksExplore exhaustive search, heuristic search, and Bayesian and multi-fidelity optimization methodsLearn which method should be used to solve a specific situation or problemBook DescriptionHyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.What you will learnDiscover hyperparameter space and types of hyperparameter distributionsExplore manual, grid, and random search, and the pros and cons of eachUnderstand powerful underdog methods along with best practicesExplore the hyperparameters of popular algorithmsDiscover how to tune hyperparameters in different frameworks and librariesDeep dive into top frameworks such as Scikit, Hyperopt, Optuna, NNI, and DEAPGet to grips with best practices that you can apply to your machine learning models right awayWho this book is forThis book is for data scientists and ML engineers who are working with Python and want to further boost their ML model’s performance by using the appropriate hyperparameter tuning method. Although a basic understanding of machine learning and how to code in Python is needed, no prior knowledge of hyperparameter tuning in Python is required.
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Build Android-Based Smart Applications
Build smart applications using cutting-edge technologies such as rules engines, code automation frameworks, and natural language processing (NLP). This book provides step-by-step instructions on how to port nine rules engines (CLIPS, JRuleEngine, DTRules, Zilonis, TermWare, Roolie, OpenRules, JxBRE, and JEOPS) to the Android platform. You’ll learn how to use each rules engine to build a smart application with sample code snippets so that you can get started with programming smart applications immediately. Build Android-Based Smart Applications also describes porting issues with other popular rules engines (Drools, JLisa, Take, and Jess).This book is a step-by-step guide on how to generate a working smart application from requirement specifications. It concludes by showing you how to generate a smart application from unstructured knowledge using the Stanford POS (Part of Speech) tagger NLP framework.What You Will LearnEvaluate the available rules engines to see which rules engine is best to use for building smart applicationsBuild smart applications using rules enginesCreate a smart application using NLPAutomatically generate smart application from requirement specificationsWho This Book Is ForAndroid and web developers with some experience.
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Kernel Methods for Machine Learning with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
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Building High Integrity Applications with SPARK
Software is pervasive in our lives. We are accustomed to dealing with the failures of much of that software - restarting an application is a very familiar solution. Such solutions are unacceptable when the software controls our cars, airplanes and medical devices or manages our private information. These applications must run without error. SPARK provides a means, based on mathematical proof, to guarantee that a program has no errors. SPARK is a formally defined programming language and a set of verification tools specifically designed to support the development of software used in high integrity applications. Using SPARK, developers can formally verify properties of their code such as information flow, freedom from runtime errors, functional correctness, security properties and safety properties. Written by two SPARK experts, this is the first introduction to the just-released 2014 version. It will help students and developers alike master the basic concepts for building systems with SPARK.
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Deep Reinforcement Learning with Python
An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithmsKey FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithmLearn how to implement algorithms with code by following examples with line-by-line explanationsExplore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrationsBook DescriptionWith significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects.What you will learnUnderstand core RL concepts including the methodologies, math, and codeTrain an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI GymTrain an agent to play Ms Pac-Man using a Deep Q NetworkLearn policy-based, value-based, and actor-critic methodsMaster the math behind DDPG, TD3, TRPO, PPO, and many othersExplore new avenues such as the distributional RL, meta RL, and inverse RLUse Stable Baselines to train an agent to walk and play Atari gamesWho this book is forIf you’re a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you.Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.
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Numerical Methods with Python
Introduces students to appropriate use of computer programming within the scientific disciplines using Python. Discusses several common applications of programming and implementation using real world examples and hands on programming exercises. Students learn how to model situations such as image recognition, medical diagnosis, spread of disease, and others. The text could be used by students and lecturers for courses in Python, Numerical Methods, or as a first course in Data Science.
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Mastering Predictive Analytics with Python
Exploit the power of data in your business by building advanced predictive modeling applications with PythonAbout This Book• Master open source Python tools to build sophisticated predictive models• Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide• Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understandingWho This Book Is ForThis book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python.What You Will Learn • Gain an insight into components and design decisions for an analytical application• Master the use Python notebooks for exploratory data analysis and rapid prototyping• Get to grips with applying regression, classification, clustering, and deep learning algorithms• Discover the advanced methods to analyze structured and unstructured data• Find out how to deploy a machine learning model in a production environment• Visualize the performance of models and the insights they produce• Scale your solutions as your data grows using Python• Ensure the robustness of your analytic applications by mastering the best practices of predictive analysisIn DetailThe volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to lifeStyle and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to
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Hands-On GPU Programming with Python and CUDA
Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook DescriptionHands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory.As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS.With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain.By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.What you will learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is forHands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
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Modernizing Oracle Tuxedo Applications with Python
Learn Tuxedo by developing solutions in Python for real-world problemsKey FeaturesUnderstand Tuxedo through a modern lens by experimenting with Python codeBoost your productivity by using Python programming to perform common Tuxedo programming tasksManage real-world integration tasks with small Python programsBook DescriptionDespite being developed in the 1980s, Oracle Tuxedo still runs a significant part of critical infrastructure and is not going away any time soon. Modernizing Oracle Tuxedo Applications with Python will help you get to grips with the most important Tuxedo concepts by writing Python code. The book starts with an introduction to Oracle Tuxedo and guides you in installing its latest version and Python bindings for Tuxedo on Linux. You'll then learn how to build your first server and client, configure Tuxedo, and start running an application. As you advance, you'll understand load balancing and work with the BBL server, which is at the heart of a Tuxedo application. This Tuxedo book will also cover Boolean expressions and different ways to export Tuxedo buffers for storage and transmission, before showing you how to implement servers and clients and use the management information base to change the configuration dynamically. Once you've learned how to configure Tuxedo for transactions and control them in application code, you'll discover how to use the store-and-forward functionality to reach destinations and use an Oracle database from a Tuxedo application. By the end of this Oracle Tuxedo book, you'll be able to perform common Tuxedo programming tasks with Python and integrate Tuxedo applications with other parts of modern infrastructure.What you will learnUnderstand Oracle Tuxedo as a microservice platformDevelop Oracle Tuxedo applications using Python 3Perform administration tasks programmatically with Python 3Extract Tuxedo statistics for monitoring application performanceIntegrate Tuxedo into the modern software ecosystemUnderstand how distributed transactions work in TuxedoWho this book is forThis book is for developers who are new to Tuxedo and are looking to develop a new modern front-end or integrate Tuxedo in their applications. The book will also help experienced Tuxedo, C or COBOL developers to improve their productivity and QA engineers to automate Tuxedo application tests. Beginner-level knowledge of Python and Linux shell is required before getting started with this book.
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