Building Ai Agents With Microsoft Semantic Kernel

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Building AI Applications with Microsoft Semantic Kernel

Author : Lucas A. Meyer
Publisher : Packt Publishing Ltd
ISBN 13 : 1835469590
Total Pages : 252 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Building AI Applications with Microsoft Semantic Kernel by : Lucas A. Meyer

Download or read book Building AI Applications with Microsoft Semantic Kernel written by Lucas A. Meyer and published by Packt Publishing Ltd. This book was released on 2024-06-21 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 examples Key Features Link your C# and Python applications with the latest AI models from OpenAI Combine and orchestrate different AI services such as text and image generators Create 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 database Purchase of the print or Kindle book includes a free PDF eBook Book 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 learn Write reusable AI prompts and connect to different AI providers Create new plugins that extend the capabilities of AI services Understand how to combine multiple plugins to execute complex actions Orchestrate multiple AI services to accomplish a task Leverage the powerful planner to automatically create appropriate AI calls Use vector databases as additional memory for your AI tasks Deploy your application to ChatGPT, making it available to hundreds of millions of users Who this book is for This 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.

Generative AI with LangChain

Author : Ben Auffarth
Publisher : Packt Publishing Ltd
ISBN 13 : 1835088368
Total Pages : 369 pages
Book Rating : 4.8/5 (35 download)

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Book Synopsis Generative AI with LangChain by : Ben Auffarth

Download or read book Generative AI with LangChain written by Ben Auffarth and published by Packt Publishing Ltd. This book was released on 2023-12-22 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: 2024 Edition – Get to grips with the LangChain framework to develop production-ready applications, including agents and personal assistants. The 2024 edition features updated code examples and an improved GitHub repository. Purchase of the print or Kindle book includes a free PDF eBook. Key Features Learn how to leverage LangChain to work around LLMs’ inherent weaknesses Delve into LLMs with LangChain and explore their fundamentals, ethical dimensions, and application challenges Get better at using ChatGPT and GPT models, from heuristics and training to scalable deployment, empowering you to transform ideas into reality Book DescriptionChatGPT and the GPT models by OpenAI have brought about a revolution not only in how we write and research but also in how we can process information. This book discusses the functioning, capabilities, and limitations of LLMs underlying chat systems, including ChatGPT and Gemini. It demonstrates, in a series of practical examples, how to use the LangChain framework to build production-ready and responsive LLM applications for tasks ranging from customer support to software development assistance and data analysis – illustrating the expansive utility of LLMs in real-world applications. Unlock the full potential of LLMs within your projects as you navigate through guidance on fine-tuning, prompt engineering, and best practices for deployment and monitoring in production environments. Whether you're building creative writing tools, developing sophisticated chatbots, or crafting cutting-edge software development aids, this book will be your roadmap to mastering the transformative power of generative AI with confidence and creativity.What you will learn Create LLM apps with LangChain, like question-answering systems and chatbots Understand transformer models and attention mechanisms Automate data analysis and visualization using pandas and Python Grasp prompt engineering to improve performance Fine-tune LLMs and get to know the tools to unleash their power Deploy LLMs as a service with LangChain and apply evaluation strategies Privately interact with documents using open-source LLMs to prevent data leaks Who this book is for The book is for developers, researchers, and anyone interested in learning more about LangChain. Whether you are a beginner or an experienced developer, this book will serve as a valuable resource if you want to get the most out of LLMs using LangChain. Basic knowledge of Python is a prerequisite, while prior exposure to machine learning will help you follow along more easily.

Building LLM Powered Applications

Author : Valentina Alto
Publisher : Packt Publishing Ltd
ISBN 13 : 1835462634
Total Pages : 343 pages
Book Rating : 4.8/5 (354 download)

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Book Synopsis Building LLM Powered Applications by : Valentina Alto

Download or read book Building LLM Powered Applications written by Valentina Alto and published by Packt Publishing Ltd. This book was released on 2024-05-22 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on with GPT 3.5, GPT 4, LangChain, Llama 2, Falcon LLM and more, to build LLM-powered sophisticated AI applications Key Features Embed LLMs into real-world applications Use LangChain to orchestrate LLMs and their components within applications Grasp basic and advanced techniques of prompt engineering Book DescriptionBuilding LLM Powered Applications delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer, ultimately paving the way for the emergence of large foundation models (LFMs) that extend the boundaries of AI capabilities. The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain, we guide you through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio. Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.What you will learn Explore the core components of LLM architecture, including encoder-decoder blocks and embeddings Understand the unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM Use AI orchestrators like LangChain, with Streamlit for the frontend Get familiar with LLM components such as memory, prompts, and tools Learn how to use non-parametric knowledge and vector databases Understand the implications of LFMs for AI research and industry applications Customize your LLMs with fine tuning Learn about the ethical implications of LLM-powered applications Who this book is for Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.

Data Science with .NET and Polyglot Notebooks

Author : Matt Eland
Publisher : Packt Publishing Ltd
ISBN 13 : 1835882978
Total Pages : 404 pages
Book Rating : 4.8/5 (358 download)

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Book Synopsis Data Science with .NET and Polyglot Notebooks by : Matt Eland

Download or read book Data Science with .NET and Polyglot Notebooks written by Matt Eland and published by Packt Publishing Ltd. This book was released on 2024-08-30 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: ProgExpand your skillset by learning how to perform data science, machine learning, and generative AI experiments in .NET Interactive notebooks using a variety of languages, including C#, F#, SQL, and PowerShell Key Features Learn Conduct a full range of data science experiments with clear explanations from start to finish Learn key concepts in data analytics, machine learning, and AI and apply them to solve real-world problems Access all of the code online as a notebook and interactive GitHub Codespace Purchase of the print or Kindle book includes a free PDF eBook Book Description As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem. What you will learn Load, analyze, and transform data using DataFrames, data visualization, and descriptive statistics Train machine learning models with ML.NET for classification and regression tasks Customize ML.NET model training pipelines with AutoML, transforms, and model trainers Apply best practices for deploying models and monitoring their performance Connect to generative AI models using Polyglot Notebooks Chain together complex AI tasks with AI orchestration, RAG, and Semantic Kernel Create interactive online documentation with Mermaid charts and GitHub Codespaces Who this book is for This book is for experienced C# or F# developers who want to transition into data science and machine learning while leveraging their .NET expertise. It’s ideal for those looking to learn ML.NET and Semantic kernel and extend their .NET skills to data science, machine learning, and Generative AI Workflows.rammer’s guide to data science using ML.NET, OpenAI, and Semantic Kernel

Programming Large Language Models with Azure Open AI

Author : Francesco Esposito
Publisher : Microsoft Press
ISBN 13 : 0138280452
Total Pages : 605 pages
Book Rating : 4.1/5 (382 download)

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Book Synopsis Programming Large Language Models with Azure Open AI by : Francesco Esposito

Download or read book Programming Large Language Models with Azure Open AI written by Francesco Esposito and published by Microsoft Press. This book was released on 2024-04-03 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software

Azure OpenAI Service for Cloud Native Applications

Author : Adrián González Sánchez
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1098154967
Total Pages : 249 pages
Book Rating : 4.0/5 (981 download)

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Book Synopsis Azure OpenAI Service for Cloud Native Applications by : Adrián González Sánchez

Download or read book Azure OpenAI Service for Cloud Native Applications written by Adrián González Sánchez and published by "O'Reilly Media, Inc.". This book was released on 2024-06-27 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service—using powerful generative AI models such as GPT-4 and GPT-4o—within the Microsoft Azure cloud computing platform. To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You'll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts. Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you: Learn how to implement cloud native applications with Azure OpenAI Service Deploy, customize, and integrate Azure OpenAI Service with your applications Customize large language models and orchestrate knowledge with company-owned data Use advanced roadmaps to plan your generative AI project Estimate cost and plan generative AI implementations for adopter companies

Deep Learning with Azure

Author : Mathew Salvaris
Publisher : Apress
ISBN 13 : 1484236793
Total Pages : 298 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Deep Learning with Azure by : Mathew Salvaris

Download or read book Deep Learning with Azure written by Mathew Salvaris and published by Apress. This book was released on 2018-08-24 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

Artificial Intelligence for Cybersecurity

Author : Bojan Kolosnjaji
Publisher : Packt Publishing Ltd
ISBN 13 : 1805123556
Total Pages : 358 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis Artificial Intelligence for Cybersecurity by : Bojan Kolosnjaji

Download or read book Artificial Intelligence for Cybersecurity written by Bojan Kolosnjaji and published by Packt Publishing Ltd. This book was released on 2024-10-31 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain well-rounded knowledge of AI methods in cybersecurity and obtain hands-on experience in implementing them to bring value to your organization Key Features Familiarize yourself with AI methods and approaches and see how they fit into cybersecurity Learn how to design solutions in cybersecurity that include AI as a key feature Acquire practical AI skills using step-by-step exercises and code examples Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionArtificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables. Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them. By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.What you will learn Recognize AI as a powerful tool for intelligence analysis of cybersecurity data Explore all the components and workflow of an AI solution Find out how to design an AI-based solution for cybersecurity Discover how to test various AI-based cybersecurity solutions Evaluate your AI solution and describe its advantages to your organization Avoid common pitfalls and difficulties when implementing AI solutions Who this book is for This book is for machine learning practitioners looking to apply their skills to overcome cybersecurity challenges. Cybersecurity workers who want to leverage machine learning methods will also find this book helpful. Fundamental concepts of machine learning and beginner-level knowledge of Python programming are needed to understand the concepts present in this book. Whether you’re a student or an experienced professional, this book offers a unique and valuable learning experience that will enable you to protect your network and data against the ever-evolving threat landscape.

Hands-On Intelligent Agents with OpenAI Gym

Author : Praveen Palanisamy
Publisher : Packt Publishing Ltd
ISBN 13 : 1788835131
Total Pages : 246 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Hands-On Intelligent Agents with OpenAI Gym by : Praveen Palanisamy

Download or read book Hands-On Intelligent Agents with OpenAI Gym written by Praveen Palanisamy and published by Packt Publishing Ltd. This book was released on 2018-07-31 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulator Key Features Explore the OpenAI Gym toolkit and interface to use over 700 learning tasks Implement agents to solve simple to complex AI problems Study learning environments and discover how to create your own Book Description Many real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken. The ability to solve such tasks without a machine being programmed requires a machine to be artificially intelligent and capable of learning to adapt. This book is an easy-to-follow guide to implementing learning algorithms for machine software agents in order to solve discrete or continuous sequential decision making and control tasks. Hands-On Intelligent Agents with OpenAI Gym takes you through the process of building intelligent agent algorithms using deep reinforcement learning starting from the implementation of the building blocks for configuring, training, logging, visualizing, testing, and monitoring the agent. You will walk through the process of building intelligent agents from scratch to perform a variety of tasks. In the closing chapters, the book provides an overview of the latest learning environments and learning algorithms, along with pointers to more resources that will help you take your deep reinforcement learning skills to the next level. What you will learn Explore intelligent agents and learning environments Understand the basics of RL and deep RL Get started with OpenAI Gym and PyTorch for deep reinforcement learning Discover deep Q learning agents to solve discrete optimal control tasks Create custom learning environments for real-world problems Apply a deep actor-critic agent to drive a car autonomously in CARLA Use the latest learning environments and algorithms to upgrade your intelligent agent development skills Who this book is for If you’re a student, game/machine learning developer, or AI enthusiast looking to get started with building intelligent agents and algorithms to solve a variety of problems with the OpenAI Gym interface, this book is for you. You will also find this book useful if you want to learn how to build deep reinforcement learning-based agents to solve problems in your domain of interest. Though the book covers all the basic concepts that you need to know, some working knowledge of Python programming language will help you get the most out of it.

An Introduction to Neural Information Retrieval

Author : Bhaskar Mitra
Publisher : Foundations and Trends (R) in Information Retrieval
ISBN 13 : 9781680835328
Total Pages : 142 pages
Book Rating : 4.8/5 (353 download)

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Book Synopsis An Introduction to Neural Information Retrieval by : Bhaskar Mitra

Download or read book An Introduction to Neural Information Retrieval written by Bhaskar Mitra and published by Foundations and Trends (R) in Information Retrieval. This book was released on 2018-12-23 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Efficient Query Processing for Scalable Web Search will be a valuable reference for researchers and developers working on This tutorial provides an accessible, yet comprehensive, overview of the state-of-the-art of Neural Information Retrieval.

The Security Development Lifecycle

Author : Michael Howard
Publisher :
ISBN 13 :
Total Pages : 364 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis The Security Development Lifecycle by : Michael Howard

Download or read book The Security Development Lifecycle written by Michael Howard and published by . This book was released on 2006 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your customers demand and deserve better security and privacy in their software. This book is the first to detail a rigorous, proven methodology that measurably minimizes security bugs--the Security Development Lifecycle (SDL). In this long-awaited book, security experts Michael Howard and Steve Lipner from the Microsoft Security Engineering Team guide you through each stage of the SDL--from education and design to testing and post-release. You get their first-hand insights, best practices, a practical history of the SDL, and lessons to help you implement the SDL in any development organization. Discover how to: Use a streamlined risk-analysis process to find security design issues before code is committed Apply secure-coding best practices and a proven testing process Conduct a final security review before a product ships Arm customers with prescriptive guidance to configure and deploy your product more securely Establish a plan to respond to new security vulnerabilities Integrate security discipline into agile methods and processes, such as Extreme Programming and Scrum Includes a CD featuring: A six-part security class video conducted by the authors and other Microsoft security experts Sample SDL documents and fuzz testing tool PLUS--Get book updates on the Web. For customers who purchase an ebook version of this title, instructions for downloading the CD files can be found in the ebook.

Artificial Intelligence and Games

Author : Georgios N. Yannakakis
Publisher : Springer
ISBN 13 : 3319635190
Total Pages : 350 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Artificial Intelligence and Games by : Georgios N. Yannakakis

Download or read book Artificial Intelligence and Games written by Georgios N. Yannakakis and published by Springer. This book was released on 2018-02-17 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading.

Learn WinUI 3.0

Author : Alvin Ashcraft
Publisher : Packt Publishing Ltd
ISBN 13 : 1800207395
Total Pages : 440 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Learn WinUI 3.0 by : Alvin Ashcraft

Download or read book Learn WinUI 3.0 written by Alvin Ashcraft and published by Packt Publishing Ltd. This book was released on 2021-03-26 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: A beginner's guide to building Windows applications with WinUI for UWP and desktop applications Key FeaturesCreate modern Windows 10 applications and gain access to UI controls that were previously limited to UWP applicationsDiscover how to modernize your existing Win32 apps with a modern Windows 10 UILearn to embed a single page application (SPA) in a WinUI application with a web framework like BlazorBook Description WinUI 3.0 takes a whole new approach to delivering Windows UI components and controls, and is able to deliver the same features on more than one version of Windows 10. Learn WinUI 3.0 is a comprehensive introduction to WinUI and Windows apps for anyone who is new to WinUI, Universal Windows Platform (UWP), and XAML applications. The book begins by helping you get to grips with the latest features in WinUI and shows you how XAML is used in UI development. You'll then set up a new Visual Studio environment and learn how to create a new UWP project. Next, you'll find out how to incorporate the Model-View-ViewModel (MVVM) pattern in a WinUI project and develop unit tests for ViewModel commands. Moving on, you'll cover the Windows Template Studio (WTS) new project wizard and WinUI libraries in a step-by-step way. As you advance, you'll discover how to leverage the Fluent Design system to create beautiful WinUI applications. You'll also explore the contents and capabilities of the Windows Community Toolkit and learn to create a new UWP user control. Toward the end, the book will teach you how to build, debug, unit test, deploy, and monitor apps in production. By the end of this book, you'll have learned how to build WinUI applications from scratch and modernize existing WPF and WinForms applications using WinUI controls. What you will learnGet up and running with WinUI and discover how it fits into the landscape of Project Reunion and Windows UI developmentBuild new Windows apps quickly with robust templatesDevelop testable and maintainable apps using the MVVM patternModernize WPF and WinForms applications with WinUI and XAML IslandsDiscover how to build apps that can target Windows and leverage the power of the webInstall the XAML Controls Gallery sample app and explore available WinUI controlsWho this book is for This book is for anyone who wants to develop Windows applications with a modern user experience (UX). If you are familiar with UWP and WPF and are looking to enhance your knowledge of Windows development and modernize existing apps, you will find this book useful. Hands-on experience with C# and .NET is expected but no prior knowledge of WinUI is required.

Developing Multi-Agent Systems with JADE

Author : Fabio Luigi Bellifemine
Publisher : John Wiley & Sons
ISBN 13 : 0470058404
Total Pages : 300 pages
Book Rating : 4.4/5 (7 download)

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Book Synopsis Developing Multi-Agent Systems with JADE by : Fabio Luigi Bellifemine

Download or read book Developing Multi-Agent Systems with JADE written by Fabio Luigi Bellifemine and published by John Wiley & Sons. This book was released on 2007-03-13 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to employ JADE to build multi-agent systems! JADE (Java Agent DEvelopment framework) is a middleware for the development of applications, both in the mobile and fixed environment, based on the Peer-to-Peer intelligent autonomous agent approach. JADE enables developers to implement and deploy multi-agent systems, including agents running on wireless networks and limited-resource devices. Developing Multi-Agent Systems with JADE is a practical guide to using JADE. The text will give an introduction to agent technologies and the JADE Platform, before proceeding to give a comprehensive guide to programming with JADE. Basic features such as creating agents, agent tasks, agent communication, agent discovery and GUIs are covered, as well as more advanced features including ontologies and content languages, complex behaviours, interaction protocols, agent mobility, and the in-process interface. Issues such as JADE internals, running JADE agents on mobile devices, deploying a fault tolerant JADE platform, and main add-ons are also covered in depth. Developing Multi-Agent Systems with JADE: Comprehensive guide to using JADE to build multi-agent systems and agent orientated programming. Describes and explains ontologies and content language, interaction protocols and complex behaviour. Includes material on persistence, security and a semantics framework. Contains numerous examples, problems, and illustrations to enhance learning. Presents a case study demonstrating the use of JADE in practice. Offers an accompanying website with additional learning resources such as sample code, exercises and PPT-slides. This invaluable resource will provide multi-agent systems practitioners, programmers working in the software industry with an interest on multi-agent systems as well as final year undergraduate and postgraduate students in CS and advanced networking and telecoms courses with a comprehensive guide to using JADE to employ multi agent systems. With contributions from experts in JADE and multi agent technology.

TensorFlow 2 Reinforcement Learning Cookbook

Author : Praveen Palanisamy
Publisher : Packt Publishing Ltd
ISBN 13 : 1838985999
Total Pages : 473 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis TensorFlow 2 Reinforcement Learning Cookbook by : Praveen Palanisamy

Download or read book TensorFlow 2 Reinforcement Learning Cookbook written by Praveen Palanisamy and published by Packt Publishing Ltd. This book was released on 2021-01-15 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover recipes for developing AI applications to solve a variety of real-world business problems using reinforcement learning Key FeaturesDevelop and deploy deep reinforcement learning-based solutions to production pipelines, products, and servicesExplore popular reinforcement learning algorithms such as Q-learning, SARSA, and the actor-critic methodCustomize and build RL-based applications for performing real-world tasksBook Description With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch. What you will learnBuild deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras APIImplement state-of-the-art deep reinforcement learning algorithms using minimal codeBuild, train, and package deep RL agents for cryptocurrency and stock tradingDeploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud servicesSpeed up agent development using distributed DNN model trainingExplore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)Who this book is for The book is for machine learning application developers, AI and applied AI researchers, data scientists, deep learning practitioners, and students with a basic understanding of reinforcement learning concepts who want to build, train, and deploy their own reinforcement learning systems from scratch using TensorFlow 2.x.

Microsoft HoloLens Developer's Guide

Author : Dennis Vroegop
Publisher : Packt Publishing Ltd
ISBN 13 : 1786464381
Total Pages : 388 pages
Book Rating : 4.7/5 (864 download)

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Book Synopsis Microsoft HoloLens Developer's Guide by : Dennis Vroegop

Download or read book Microsoft HoloLens Developer's Guide written by Dennis Vroegop and published by Packt Publishing Ltd. This book was released on 2017-07-21 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transform the ways you communicate, create, collaborate, and explore using Microsoft HoloLens About This Book Create immersive augmented reality apps for Microsoft HoloLens from scratch Leverage the powerful HoloLens sensors to interact with real-world motions and gestures and make your app life-like Explore the powerful Unity 5 SDK along with the Windows Unified platform to get the most out of your HoloLens app Who This Book Is For If you are a developer who wants to create augmented reality apps for the Microsoft HoloLens platform, then this is the book for you. Coding experience with C# is assumed. What You Will Learn Design an app for HoloLens that is feasible and attractive to use Add gestures and interact with them Create sounds in the app and place them in a 3D space Use voice generation and voice recognition to make your apps more lifelike Interact with the physical environment to place holograms on top of physical objects Compare HoloLens with the other products and know how to use its strengths Use assets from third parties to enrich our app In Detail HoloLens, Microsoft's innovative augmented reality headset, overlaps holograms into a user's vision of their environment. Your ideas are closer to becoming real when you can create and work with holograms in relation to the world around you. If you are dreaming beyond virtual worlds, beyond screens, beyond pixels, and want to take a big leap in the world of augmented reality, then this is the book you want. Starting off with brainstorming and the design process, you will take your first steps in creating your application for HoloLens. You will learn to add gestures and write an app that responds to verbal commands before gradually moving on creating sounds in the app and placing them in a 3D space. You will then communicate between devices in the boundaries of the UWP model. Style and approach This book takes a step-by-step, practical, tutorial-style approach where you will dive deep into HoloLens app development. You will work with the API and write your own complex scripts that would interact with the powerful HoloLens sensors and with realistic examples, you will be able to create immersive 3D apps for HoloLens.

Deep Learning with Python

Author : Francois Chollet
Publisher : Simon and Schuster
ISBN 13 : 1638352046
Total Pages : 597 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Deep Learning with Python by : Francois Chollet

Download or read book Deep Learning with Python written by Francois Chollet and published by Simon and Schuster. This book was released on 2017-11-30 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author François Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance