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Building AI Intensive Python Applications
Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI appsKey FeaturesGet to grips with the fundamentals of LLMs, vector databases, and Python frameworksImplement effective retrieval-augmented generation strategies with MongoDB AtlasOptimize AI models for performance and accuracy with model compression and deployment optimizationPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications.The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance.By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.What you will learnUnderstand the architecture and components of the generative AI stackExplore the role of vector databases in enhancing AI applicationsMaster Python frameworks for AI developmentImplement Vector Search in AI applicationsFind out how to effectively evaluate LLM outputOvercome common failures and challenges in AI developmentWho this book is forThis book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
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Building Data Science Applications with FastAPI
Learn all the features and best practices of FastAPI to build, deploy, and monitor powerful data science and AI apps, like object detection or image generation.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesUncover the secrets of FastAPI, including async I/O, type hinting, and dependency injectionLearn to add authentication, authorization, and interaction with databases in a FastAPI backendDevelop real-world projects using pre-trained AI modelsBook DescriptionBuilding Data Science Applications with FastAPI is the go-to resource for creating efficient and dependable data science API backends. This second edition incorporates the latest Python and FastAPI advancements, along with two new AI projects – a real-time object detection system and a text-to-image generation platform using Stable Diffusion. The book starts with the basics of FastAPI and modern Python programming. You'll grasp FastAPI's robust dependency injection system, which facilitates seamless database communication, authentication implementation, and ML model integration. As you progress, you'll learn testing and deployment best practices, guaranteeing high-quality, resilient applications. Throughout the book, you'll build data science applications using FastAPI with the help of projects covering common AI use cases, such as object detection and text-to-image generation. These hands-on experiences will deepen your understanding of using FastAPI in real-world scenarios. By the end of this book, you'll be well equipped to maintain, design, and monitor applications to meet the highest programming standards using FastAPI, empowering you to create fast and reliable data science API backends with ease while keeping up with the latest advancements.What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkDeploy a performant and reliable web backend for a data science applicationIntegrate common Python data science libraries into a web backendIntegrate an object detection algorithm into a FastAPI backendBuild a distributed text-to-image AI system with Stable DiffusionAdd metrics and logging and learn how to monitor themWho this book is forThis book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
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Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
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Building AI Applications with Microsoft Semantic Kernel
Unlock the power of GenAI by effortlessly linking your C# and Python apps with cutting-edge models, orchestrating diverse AI services with finesse, and crafting bespoke applications through immersive, real-world examplesKey FeaturesLink your C# and Python applications with the latest AI models from OpenAICombine and orchestrate different AI services such as text and image generatorsCreate your own AI apps with real-world use case examples that show you how to use basic generative AI, create images, process documents, use a vector databasePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the fast-paced world of AI, developers are constantly seeking efficient ways to integrate AI capabilities into their apps. Microsoft Semantic Kernel simplifies this process by using the GenAI features from Microsoft and OpenAI.Written by Lucas A. Meyer, a Principal Research Scientist in Microsoft’s AI for Good Lab, this book helps you get hands on with Semantic Kernel. It begins by introducing you to different generative AI services such as GPT-3.5 and GPT-4, demonstrating their integration with Semantic Kernel. You’ll then learn to craft prompt templates for reuse across various AI services and variables. Next, you’ll learn how to add functionality to Semantic Kernel by creating your own plugins. The second part of the book shows you how to combine multiple plugins to execute complex actions, and how to let Semantic Kernel use its own AI to solve complex problems by calling plugins, including the ones made by you. The book concludes by teaching you how to use vector databases to expand the memory of your AI services and how to help AI remember the context of earlier requests. You’ll also be guided through several real-world examples of applications, such as RAG and custom GPT agents.By the end of this book, you'll have gained the knowledge you need to start using Semantic Kernel to add AI capabilities to your applications.What you will learnWrite reusable AI prompts and connect to different AI providersCreate new plugins that extend the capabilities of AI servicesUnderstand how to combine multiple plugins to execute complex actionsOrchestrate multiple AI services to accomplish a taskLeverage the powerful planner to automatically create appropriate AI callsUse vector databases as additional memory for your AI tasksDeploy your application to ChatGPT, making it available to hundreds of millions of usersWho this book is forThis book is for beginner-level to experienced .NET or Python software developers who want to quickly incorporate the latest AI technologies into their applications, without having to learn the details of every new AI service. Product managers with some development experience will find this book helpful while creating proof-of-concept applications. This book requires working knowledge of programming basics.
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Building AI Applications with OpenAI APIs
Improve your app development skills by building a ChatGPT clone, code bug fixer, quiz generator, translation app, email auto-reply, PowerPoint generator, and moreKey FeaturesTransition into an expert AI developer by mastering ChatGPT concepts, including fine-tuning and integrationsGain hands-on experience through real-world projects covering a wide range of AI applicationsImplement payment systems in your applications by integrating the ChatGPT API with StripePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnlock the power of AI in your applications with ChatGPT with this practical guide that shows you how to seamlessly integrate OpenAI APIs into your projects, enabling you to navigate complex APIs and ensure seamless functionality with ease.This new edition is updated with key topics such as OpenAI Embeddings, which’ll help you understand the semantic relationships between words and phrases. You’ll find out how to use ChatGPT, Whisper, and DALL-E APIs through 10 AI projects using the latest OpenAI models, GPT-3.5, and GPT-4, with Visual Studio Code as the IDE. Within these projects, you’ll integrate ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt. You’ll get to grips with NLP tasks, build a ChatGPT clone, and create an AI code bug-fixing SaaS app. The chapters will also take you through speech recognition, text-to-speech capabilities, language translation, generating email replies, creating PowerPoint presentations, and fine-tuning ChatGPT, along with adding payment methods by integrating the ChatGPT API with Stripe.By the end of this book, you’ll be able to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.What you will learnDevelop a solid foundation in using the OpenAI API for NLP tasksBuild, deploy, and integrate payments into various desktop and SaaS AI applicationsIntegrate ChatGPT with frameworks such as Flask, Django, and Microsoft Office APIsUnleash your creativity by integrating DALL-E APIs to generate stunning AI art within your desktop appsExperience the power of Whisper API's speech recognition and text-to-speech featuresFind out how to fine-tune ChatGPT models for your specific use caseMaster AI embeddings to measure the relatedness of text stringsWho this book is forThis book is for a diverse range of professionals, including programmers, entrepreneurs, and software enthusiasts. Beginner programmers, Python developers exploring AI applications with ChatGPT, software developers integrating AI technology, and web developers creating AI-powered web applications with ChatGPT will find this book beneficial. Scholars and researchers working on AI projects with ChatGPT will also find it valuable. Basic knowledge of Python and familiarity with APIs is needed to understand the topics covered in this book.
R 1,368.00
Generative AI Foundations in Python
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsKey FeaturesGain expertise in prompt engineering, LLM fine-tuning, and domain adaptationUse transformers-based LLMs and diffusion models to implement AI applicationsDiscover strategies to optimize model performance, address ethical considerations, and build trust in AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application.Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You’ll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you’ll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs.By the end of this book, you’ll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly.What you will learnDiscover the fundamentals of GenAI and its foundations in NLPDissect foundational generative architectures including GANs, transformers, and diffusion modelsFind out how to fine-tune LLMs for specific NLP tasksUnderstand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as financeExplore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAGImplement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputsWho this book is forThis book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.
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Building Data Science Applications with FastAPI
Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applicationsKey FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook DescriptionFastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples.This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client.By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI.What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is forThis Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
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Building Chatbots with Python
Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you. The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment.The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learn to predict conditions for your bot and develop a conversational agent as a web application. Finally you will deploy your chatbot on your own server with AWS. What You Will LearnGain the basics of natural language processing using Python Collect data and train your data for the chatbotBuild your chatbot from scratch as a web appIntegrate your chatbots with Facebook, Slack, and TelegramDeploy chatbots on your own serverWho This Book Is ForIntermediate Python developers who have no idea about chatbots. Developers with basic Python programming knowledge can also take advantage of the book.
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Generative AI with Python and TensorFlow 2
Fun and exciting projects to learn what artificial minds can createKey FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI?In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks.There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation.What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is forThis is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.
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Building Python Microservices with FastAPI
Discover the secrets of building Python microservices using the FastAPI frameworkKey FeaturesProvides a reference that contains definitions, illustrations, comparative analysis, and the implementation of real-world appsCovers concepts, core details, and advanced integration and design-related topicsImparts context, app templates, suggestions, and insights that are helpful to actual projectsBook DescriptionFastAPI is an Asynchronous Server Gateway Interface (ASGI)-based framework that can help build modern, manageable, and fast microservices. Because of its asynchronous core platform, this ASGI-based framework provides the best option when it comes to performance, reliability, and scalability over the WSGI-based Django and Flask. When working with Python, Flask, and Django microservices, you’ll be able to put your knowledge to work with this practical guide to building seamlessly manageable and fast microservices.You’ll begin by understanding the background of FastAPI and learning how to install, configure, and use FastAPI to decompose business units. You’ll explore a unique and asynchronous REST API framework that can provide a better option when it comes to building microservices. After that, this book will guide you on how to apply and translate microservices design patterns in building various microservices applications and RESTful APIs using the FastAPI framework.By the end of this microservices book, you’ll be able to understand, build, deploy, test, and experiment with microservices and their components using the FastAPI framework.What you will learnUnderstand, orient, and implement REST APIs using the basic components of the FastAPI frameworkBuild asynchronous as well as synchronous REST services using the built-in pydantic module and asyncio supportCreate small-scale and large-scale microservices applications using features supported by FastAPIBuild event-driven and message-driven applications using the frameworkCreate an asynchronous and synchronous data layer with both relational and NoSQL databasesPerform numerical and symbolic computations with FastAPIWho this book is forThis book is for Python web developers, advanced Python developers, and backend developers using Flask or Django who want to learn how to use the FastAPI framework to implement microservices. Readers familiar with the REST API and microservices will also benefit from this book. Some parts of the book contain general concepts, processes, and instructions that intermediate-level developers and Python enthusiasts can relate to as well.
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Learn Python by Building Data Science Applications
Understand the constructs of the Python programming language and use them to build data science projectsKey FeaturesLearn the basics of developing applications with Python and deploy your first data applicationTake your first steps in Python programming by understanding and using data structures, variables, and loopsDelve into Jupyter, NumPy, Pandas, SciPy, and sklearn to explore the data science ecosystem in PythonBook DescriptionPython is the most widely used programming language for building data science applications. Complete with step-by-step instructions, this book contains easy-to-follow tutorials to help you learn Python and develop real-world data science projects. The “secret sauce” of the book is its curated list of topics and solutions, put together using a range of real-world projects, covering initial data collection, data analysis, and production.This Python book starts by taking you through the basics of programming, right from variables and data types to classes and functions. You’ll learn how to write idiomatic code and test and debug it, and discover how you can create packages or use the range of built-in ones. You’ll also be introduced to the extensive ecosystem of Python data science packages, including NumPy, Pandas, scikit-learn, Altair, and Datashader. Furthermore, you’ll be able to perform data analysis, train models, and interpret and communicate the results. Finally, you’ll get to grips with structuring and scheduling scripts using Luigi and sharing your machine learning models with the world as a microservice.By the end of the book, you’ll have learned not only how to implement Python in data science projects, but also how to maintain and design them to meet high programming standards.What you will learnCode in Python using Jupyter and VS CodeExplore the basics of coding – loops, variables, functions, and classesDeploy continuous integration with Git, Bash, and DVCGet to grips with Pandas, NumPy, and scikit-learnPerform data visualization with Matplotlib, Altair, and DatashaderCreate a package out of your code using poetry and test it with PyTestMake your machine learning model accessible to anyone with the web APIWho this book is forIf you want to learn Python or data science in a fun and engaging way, this book is for you. You’ll also find this book useful if you’re a high school student, researcher, analyst, or anyone with little or no coding experience with an interest in the subject and courage to learn, fail, and learn from failing. A basic understanding of how computers work will be useful.
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Building Web Applications with Python and Neo4j
About This BookDevelop a set of common applications and solutions with Neo4j and PythonSecure and deploy the Neo4j database in productionA step-by-step guide on implementing and deploying interactive Python-based web applications on graph data modelWho This Book Is ForIf you are a Python developer and want to expand your understanding of Python-based web applications over Neo4j graph data models, this is the book for you.What You Will LearnUnderstand the licensing and installation of the Neo4j database and work with its various tools and utilitiesLearn the intricacies of Cypher as a graph query languageWork with Cypher to create and modify graph data modelsIntegrate Python and Neo4j using Py2neoDevelop REST-based services over social network data using Flask and object graph models over Neo4jIntegrate Django-based web applications over graph data models using NeomodelExplore different deployment models and their applicability with existing applicationsIn DetailPy2neo is a simple and pragmatic Python library that provides access to the popular graph database Neo4j via its RESTful web service interface. This brings with it a heavily refactored core, a cleaner API, better performance, and some new idioms.You will begin with licensing and installing Neo4j, learning the fundamentals of Cypher as a graph query language, and exploring Cypher optimizations. You will discover how to integrate with various Python frameworks such as Flask and its extensions: Py2neo, Neomodel, and Django. Finally, the deployment aspects of your Python-based Neo4j applications in a production environment is also covered. By sequentially working through the steps in each chapter, you will quickly learn and master the various implementation details and integrations of Python and Neo4j, helping you to develop your use cases more quickly.
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Building ETL Pipelines with Python
Develop production-ready ETL pipelines by leveraging Python libraries and deploying them for suitable use casesKey FeaturesUnderstand how to set up a Python virtual environment with PyCharmLearn functional and object-oriented approaches to create ETL pipelinesCreate robust CI/CD processes for ETL pipelinesPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionModern extract, transform, and load (ETL) pipelines for data engineering have favored the Python language for its broad range of uses and a large assortment of tools, applications, and open source components. With its simplicity and extensive library support, Python has emerged as the undisputed choice for data processing.In this book, you’ll walk through the end-to-end process of ETL data pipeline development, starting with an introduction to the fundamentals of data pipelines and establishing a Python development environment to create pipelines. Once you've explored the ETL pipeline design principles and ET development process, you'll be equipped to design custom ETL pipelines. Next, you'll get to grips with the steps in the ETL process, which involves extracting valuable data; performing transformations, through cleaning, manipulation, and ensuring data integrity; and ultimately loading the processed data into storage systems. You’ll also review several ETL modules in Python, comparing their pros and cons when building data pipelines and leveraging cloud tools, such as AWS, to create scalable data pipelines. Lastly, you’ll learn about the concept of test-driven development for ETL pipelines to ensure safe deployments.By the end of this book, you’ll have worked on several hands-on examples to create high-performance ETL pipelines to develop robust, scalable, and resilient environments using Python.What you will learnExplore the available libraries and tools to create ETL pipelines using PythonWrite clean and resilient ETL code in Python that can be extended and easily scaledUnderstand the best practices and design principles for creating ETL pipelinesOrchestrate the ETL process and scale the ETL pipeline effectivelyDiscover tools and services available in AWS for ETL pipelinesUnderstand different testing strategies and implement them with the ETL processWho this book is forIf you are a data engineer or software professional looking to create enterprise-level ETL pipelines using Python, this book is for you. Fundamental knowledge of Python is a prerequisite.
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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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Practical Explainable AI Using Python
Learn the ins and outs of decisions, biases, and reliability of AI algorithms and how to make sense of these predictions. This book explores the so-called black-box models to boost the adaptability, interpretability, and explainability of the decisions made by AI algorithms using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers.You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decisionFurther, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, you will be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.What You'll LearnReview the different ways of making an AI model interpretable and explainableExamine the biasness and good ethical practices of AI modelsQuantify, visualize, and estimate reliability of AI modelsDesign frameworks to unbox the black-box modelsAssess the fairness of AI modelsUnderstand the building blocks of trust in AI modelsIncrease the level of AI adoptionWho This Book Is ForAI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
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Learning Python Application Development
Take Python beyond scripting to build robust, reusable, and efficient applicationsAbout This Book• Get to grips with Python techniques that address commonly encountered problems in general application development.• Develop, package, and deploy efficient applications in a fun way.• All-practical coverage of the major areas of application development, including best practices, exception handling, testing, refactoring, design patterns, performance, and GUI application development.Who This Book Is ForDo you know the basics of Python and object oriented programming? Do you want to go an extra mile and learn techniques to make your Python application robust, extensible, and efficient? Then this book is for you.What You Will Learn• Build a robust application by handling exceptions.• Modularize, package, and release the source distribution.• Document the code and implement coding standards.• Create automated tests to catch bugs in the early development stage.• Identify and re-factor badly written code to improve application life.• Detect recurring problems in the code and apply design patterns.• Improve code efficiency by identifying performance bottlenecks and fixing them.• Develop simple GUI applications using Python.In DetailPython is one of the most widely used dynamic programming languages, supported by a rich set of libraries and frameworks that enable rapid development. But fast paced development often comes with its own baggage that could bring down the quality, performance, and extensibility of an application. This book will show you ways to handle such problems and write better Python applications.From the basics of simple command-line applications, develop your skills all the way to designing efficient and advanced Python apps. Guided by a light-hearted fantasy learning theme, overcome the real-world problems of complex Python development with practical solutions. Beginning with a focus on robustness, packaging, and releasing application code, you'll move on to focus on improving application lifetime by making code extensible, reusable, and readable. Get to grips with Python refactoring, design patterns and best practices. Techniques to identify the bottlenecks and improve performance are covered in a series of chapters devoted to performance, before closing with a look at developing Python GUIs.Style and approachThe book uses a fantasy game theme as a medium to explain various topics. Specific aspects of application development are explained in different chapters. In each chapter the reader is presented with an interesting problem which is then tackled using hands-on examples with easy-to-follow instructions.
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Hands-On Enterprise Application Development with Python
Architect scalable, reliable, and maintainable applications for enterprises with PythonKey FeaturesExplore various Python design patterns used for enterprise software developmentApply best practices for testing and performance optimization to build stable applicationsLearn about different attacking strategies used on enterprise applications and how to avoid themBook DescriptionDynamically typed languages like Python are continuously improving. With the addition of exciting new features and a wide selection of modern libraries and frameworks, Python has emerged as an ideal language for developing enterprise applications. Hands-On Enterprise Application Development with Python will show you how to build effective applications that are stable, secure, and easily scalable.The book is a detailed guide to building an end-to-end enterprise-grade application in Python. You will learn how to effectively implement Python features and design patterns that will positively impact your application lifecycle. The book also covers advanced concurrency techniques that will help you build a RESTful application with an optimized frontend. Given that security and stability are the foundation for an enterprise application, you’ll be trained on effective testing, performance analysis, and security practices, and understand how to embed them in your codebase during the initial phase. You’ll also be guided in how to move on from a monolithic architecture to one that is service oriented, leveraging microservices and serverless deployment techniques.By the end of the book, you will have become proficient at building efficient enterprise applications in Python.What you will learnUnderstand the purpose of design patterns and their impact on application lifecycleBuild applications that can handle large amounts of data-intensive operationsUncover advanced concurrency techniques and discover how to handle a large number of requests in productionOptimize frontends to improve the client-side experience of your applicationEffective testing and performance profiling techniques to detect issues in applications early in the development cycleBuild applications with a focus on securityImplement large applications as microservices to improve scalabilityWho this book is forIf you’re a developer who wants to build enterprise-grade applications, this book is for you. Basic to intermediate-level of programming experience with Python and database systems is required to understand the concepts covered in this book.
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R 672.00
Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops
This book constitutes the refereed proceedings of five International Workshops held as parallel events of the 18th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2022, virtually and in Hersonissos, Crete, Greece, in June 2022: the 11th Mining Humanistic Data Workshop (MHDW 2022); the 7th 5G-Putting Intelligence to the Network Edge Workshop (5G-PINE 2022); the 1st workshop on AI in Energy, Building and Micro-Grids (AIBMG 2022); the 1st Workshop/Special Session on Machine Learning and Big Data in Health Care (ML@HC 2022); and the 2nd Workshop on Artificial Intelligence in Biomedical Engineering and Informatics (AIBEI 2022).The 35 full papers presented at these workshops were carefully reviewed and selected from 74 submissions.
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Artificial Intelligence Applications and Innovations. AIAI 2023 IFIP WG 12.5 International Workshops
This book constitutes the refereed proceedings of four International Workshops, held as parallel events of the 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023, held in León, Spain, during June 14–17, 2023: the 12th Workshop on Mining Humanistic Data (MHDW 2023); the 8th Workshop on “5G–Putting Intelligence to the Network Edge (5G-PINE 2023); the second Workshop on AI in Energy, Buildings and Micro-Grids Workshop (ΑΙBMG 2023); and the First Workshop on Visual Analytics Approaches for Complex Problems in Engineering and Biomedicine" (VAA-CP-EB 2023). This event was held in hybrid mode.The 37 regular papers presented at these workshops were carefully reviewed and selected from 86 submissions.
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Deep Learning with Applications Using Python
Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. What You Will Learn Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.Use face recognition and face detection capabilitiesCreate speech-to-text and text-to-speech functionalityEngage with chatbots using deep learningWho This Book Is ForData scientists and developers who want to adapt and build deep learning applications.
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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.
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Artificial Intelligence Applications and Innovations. AIAI 2022 IFIP WG 12.5 International Workshops
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R 489.00
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.
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