Download or read book Hands On Artificial Intelligence for Beginners written by Patrick D. Smith and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease Key FeaturesEnter the world of AI with the help of solid concepts and real-world use casesExplore AI components to build real-world automated intelligenceBecome well versed with machine learning and deep learning conceptsBook Description Virtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world. Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You'll explore feedforward, recurrent, convolutional, and generative neural networks (FFNNs, RNNs, CNNs, and GNNs), as well as reinforcement learning methods. In the concluding chapters, you'll learn how to implement these methods for a variety of tasks, such as generating text for chatbots, and playing board and video games. By the end of this book, you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications. What you will learnUse TensorFlow packages to create AI systemsBuild feedforward, convolutional, and recurrent neural networksImplement generative models for text generationBuild reinforcement learning algorithms to play gamesAssemble RNNs, CNNs, and decoders to create an intelligent assistantUtilize RNNs to predict stock market behaviorCreate and scale training pipelines and deployment architectures for AI systemsWho this book is for This book is designed for beginners in AI, aspiring AI developers, as well as machine learning enthusiasts with an interest in leveraging various algorithms to build powerful AI applications.

Download or read book Generative AI Hands On written by Anand Vemula and published by Independently Published. This book was released on 2024-07-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI Hands-On: A Practical Guide to Model Development and Real-World Applications is a hands-on resource designed to equip readers with the skills and knowledge needed to build and apply generative AI models effectively. This comprehensive guide covers the entire process, from foundational concepts to advanced techniques, offering practical insights into real-world applications. The book begins with an introduction to generative AI, explaining core concepts and its evolution. It outlines how generative models differ from other AI approaches and explores their diverse applications in fields such as text generation, image synthesis, and audio creation. Readers will gain a solid understanding of how these models work and their potential uses. Part II focuses on the technical foundation of generative models, including machine learning basics, neural networks, and deep learning techniques. It delves into key types of generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and autoregressive models, providing a clear explanation of their functionalities and differences. The practical aspect of the book is emphasized in Part III, which guides readers through setting up their environment, including software, tools, and libraries. It covers data preparation, model implementation, and fine-tuning, allowing readers to create their own generative models. Part IV showcases practical applications, offering hands-on projects for text, image, and audio generation. Case studies highlight the impact of generative AI in various domains, demonstrating its versatility and creativity. The final part addresses ethical considerations and future trends in generative AI, covering topics such as bias, fairness, and emerging technologies. This ensures readers are prepared to navigate the evolving landscape responsibly. "Generative AI Hands-On" is an essential guide for anyone looking to harness the power of generative AI for practical and innovative applications.

Download or read book Generative AI with LangChain A Hands on Approach written by Anand Vemula and published by Anand Vemula. This book was released on with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the ever-evolving world of Artificial Intelligence (AI), Generative AI stands out for its ability to create entirely new data, from realistic images to compelling music. This book equips you to harness this power, guiding you through the fundamentals and practical applications with LangChain, a user-friendly framework. Part 1 establishes the groundwork. You'll delve into the core concepts of Generative AI, including Deep Learning and Natural Language Processing (NLP). This foundational knowledge empowers you to understand how AI learns from vast datasets and generates novel outputs. Part 2 dives into the specific techniques behind Generative AI. Explore powerful methods like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), grasping how they create realistic data through innovative training processes. You'll also discover the transformative potential of Transformer-based models, particularly adept at handling text-based tasks. LangChain enters the scene in Part 3. This framework simplifies the development and deployment of Generative AI applications. Learn how LangChain streamlines the process, from selecting the appropriate model to integrating it with real-world data sources and managing its outputs. Practical guidance, including code examples and tutorials, empowers you to build your own generative applications with LangChain. Part 4 showcases the exciting possibilities. Witness how LangChain can be applied to Text Generation tasks like creating summaries or crafting engaging creative content. Explore how it facilitates Image Generation, from photorealistic synthesis to image editing and enhancement. Beyond text and images, the book delves into other applications like Music Generation, Code Generation, and even Drug Discovery, highlighting the vast potential of Generative AI. The final part, The Future of Generative AI, emphasizes the critical aspects of responsible development. You'll explore ethical considerations like bias and potential misuse, while also learning about advancements in research and how LangChain can evolve to meet these challenges. By combining foundational knowledge with practical tools and real-world applications, it empowers you to become an active participant in the Generative AI revolution.

Download or read book Hands On Generative Adversarial Networks with Keras written by Rafael Valle and published by Packt Publishing Ltd. This book was released on 2019-05-03 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Develop generative models for a variety of real-world use-cases and deploy them to production Key FeaturesDiscover various GAN architectures using Python and Keras libraryUnderstand how GAN models function with the help of theoretical and practical examplesApply your learnings to become an active contributor to open source GAN applicationsBook Description Generative Adversarial Networks (GANs) have revolutionized the fields of machine learning and deep learning. This book will be your first step towards understanding GAN architectures and tackling the challenges involved in training them. This book opens with an introduction to deep learning and generative models, and their applications in artificial intelligence (AI). You will then learn how to build, evaluate, and improve your first GAN with the help of easy-to-follow examples. The next few chapters will guide you through training a GAN model to produce and improve high-resolution images. You will also learn how to implement conditional GANs that give you the ability to control characteristics of GAN outputs. You will build on your knowledge further by exploring a new training methodology for progressive growing of GANs. Moving on, you'll gain insights into state-of-the-art models in image synthesis, speech enhancement, and natural language generation using GANs. In addition to this, you'll be able to identify GAN samples with TequilaGAN. By the end of this book, you will be well-versed with the latest advancements in the GAN framework using various examples and datasets, and you will have the skills you need to implement GAN architectures for several tasks and domains, including computer vision, natural language processing (NLP), and audio processing. Foreword by Ting-Chun Wang, Senior Research Scientist, NVIDIA What you will learnLearn how GANs work and the advantages and challenges of working with themControl the output of GANs with the help of conditional GANs, using embedding and space manipulationApply GANs to computer vision, NLP, and audio processingUnderstand how to implement progressive growing of GANsUse GANs for image synthesis and speech enhancementExplore the future of GANs in visual and sonic artsImplement pix2pixHD to turn semantic label maps into photorealistic imagesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking for a perfect mix of theory and hands-on content in order to implement GANs using Keras. Working knowledge of Python is expected.

Download or read book Hands On Explainable AI XAI with Python written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2020-07-31 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications

Download or read book Generative AI Projects written by Anand Vemula and published by Independently Published. This book was released on 2024-07-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI Projects: A Hands-On Guide is an immersive and practical resource designed to take readers on a journey through the fascinating world of generative artificial intelligence. This comprehensive guide covers the fundamental concepts and advanced techniques necessary for building and deploying generative AI models across various domains. Starting with an introduction to generative AI, the book explains the core principles, history, and evolution of this cutting-edge technology. It delves into key concepts such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs), providing readers with a solid foundation to understand how these models generate new data. The book is structured around a series of hands-on projects that progressively build in complexity. Each project is designed to be both educational and practical, offering step-by-step instructions and code examples. Project 1: Text Generation with LLMs focuses on building and fine-tuning language models for generating coherent and contextually relevant text. Readers will learn about data collection, model training, and deployment techniques. Project 2: Image Generation with GANs guides readers through the process of creating high-quality images using GANs. It covers the fundamentals of GANs, training procedures, and methods to evaluate and enhance image quality. Project 3: Music Generation with VAEs explores how to generate musical compositions. This project includes data representation for music, building VAEs, and integrating generated music into applications. Project 4: Video Generation and Synthesis tackles the complexities of video generation, including data preprocessing, model training, and evaluation of generated videos for use in entertainment and media. Advanced projects delve deeper into specific applications, such as generative art, chatbots, and healthcare. Each section emphasizes real-world applications, ethical considerations, and best practices for deploying and monitoring generative AI models. This guide is ideal for AI enthusiasts, data scientists, and developers looking to expand their knowledge and skills in generative AI. With a focus on practical implementation and real-world applications, it equips readers with the tools and knowledge to innovate and excel in the field of generative AI.

Download or read book Generative AI with Python and TensorFlow 2 written by Joseph Babcock and published by Packt Publishing Ltd. This book was released on 2021-04-30 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun and exciting projects to learn what artificial minds can create Key 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 Description Machines 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 for This 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.

Download or read book Hands On Artificial Intelligence for IoT written by Amita Kapoor and published by Packt Publishing Ltd. This book was released on 2019-01-31 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build smarter systems by combining artificial intelligence and the Internet of Things—two of the most talked about topics today Key FeaturesLeverage the power of Python libraries such as TensorFlow and Keras to work with real-time IoT dataProcess IoT data and predict outcomes in real time to build smart IoT modelsCover practical case studies on industrial IoT, smart cities, and home automationBook Description There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book starts by covering the process of gathering and preprocessing IoT data gathered from distributed sources. You will learn different AI techniques such as machine learning, deep learning, reinforcement learning, and natural language processing to build smart IoT systems. You will also leverage the power of AI to handle real-time data coming from wearable devices. As you progress through the book, techniques for building models that work with different kinds of data generated and consumed by IoT devices such as time series, images, and audio will be covered. Useful case studies on four major application areas of IoT solutions are a key focal point of this book. In the concluding chapters, you will leverage the power of widely used Python libraries, TensorFlow and Keras, to build different kinds of smart AI models. By the end of this book, you will be able to build smart AI-powered IoT apps with confidence. What you will learnApply different AI techniques including machine learning and deep learning using TensorFlow and KerasAccess and process data from various distributed sourcesPerform supervised and unsupervised machine learning for IoT dataImplement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platformsForecast time-series data using deep learning methodsImplementing AI from case studies in Personal IoT, Industrial IoT, and Smart CitiesGain unique insights from data obtained from wearable devices and smart devicesWho this book is for If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book.

Download or read book Hands On Artificial Intelligence on Amazon Web Services written by Subhashini Tripuraneni and published by . This book was released on 2019-10-04 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key Features Explore popular machine learning and deep learning services with their underlying algorithms Discover readily available artificial intelligence(AI) APIs on AWS like Vision and Language Services Design robust architectures to enable experimentation, extensibility, and maintainability of AI apps Book Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to solve real-world problems. You'll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You'll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you'll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you'll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learn Gain useful insights into different machine and deep learning models Build and deploy robust deep learning systems to production Train machine and deep learning models with diverse infrastructure specifications Scale AI apps without dealing with the complexity of managing the underlying infrastructure Monitor and Manage AI experiments efficiently Create AI apps using AWS pre-trained AI services Who this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected.

Download or read book Generative AI Foundations in Python written by Carlos Rodriguez and published by Packt Publishing Ltd. This book was released on 2024-07-26 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book 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 learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This 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.

Download or read book Practical Generative AI with Python written by Anand Vemula and published by Anand Vemula. This book was released on with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals of generative AI, providing an in-depth understanding of key concepts, algorithms, and techniques that power AI-driven content creation. Starting with an introduction to the basics of generative AI, the book explains the theoretical foundations and evolution of generative models, highlighting the significance of this technology in various domains such as image synthesis, text generation, and more. Readers will explore the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and understand their role in the development of generative models. The guide dives into essential Python libraries like TensorFlow, PyTorch, NumPy, and Pandas, offering a hands-on approach to building generative models from scratch. Each chapter is packed with practical examples, case studies, and real-world scenarios that demonstrate the application of these models in various fields, including art, music, and conversational AI. Key topics include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, autoregressive models, and transformer-based models like GPT. The book also addresses the ethical considerations surrounding generative AI, providing insights into the challenges of bias, fairness, and misinformation. Readers will benefit from step-by-step tutorials that guide them through the process of implementing and optimizing generative models, complete with code examples and hands-on exercises. Additionally, the book offers advanced techniques for improving model performance and stability, ensuring that readers are well-prepared to tackle complex AI projects. Whether you're a beginner looking to understand the basics of generative AI or an experienced developer aiming to enhance your skills, "Mastering Generative AI with Python: A Hands-On Guide" serves as an essential resource for anyone interested in the rapidly evolving field of generative AI.

Download or read book INSIDE GENERATIVE AI written by Rick Spair and published by Rick Spair. This book was released on with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI represents a groundbreaking frontier in the realm of artificial intelligence, where machines not only learn from data but also create new data, mimicking the inventive processes of human creativity. This book is a comprehensive guide that explores the depths of generative AI, from foundational concepts to advanced applications, and provides a rich array of hands-on projects and real-world case studies. Why Generative AI? In recent years, generative AI has transformed from a niche area of research to a central pillar of AI innovation, with profound implications for various industries. From generating realistic images and videos to composing music and writing compelling narratives, generative AI models are pushing the boundaries of what machines can do. This evolution has not only expanded the capabilities of AI but also sparked new forms of creative expression and problem-solving. Generative AI's impact is evident in numerous fields: Art and Design: Artists and designers are leveraging AI to create stunning visual artworks, intricate designs, and immersive digital environments. Tools like DeepDream and GauGAN have opened new horizons in artistic creativity, enabling the generation of unique and surreal visuals. Media and Entertainment: The media industry is using generative AI to automate content creation, from news articles to movie scripts, and even to generate entire virtual worlds for video games and virtual reality experiences. AI-generated music and soundtracks are also becoming increasingly popular, offering new ways to enhance auditory experiences. Healthcare: In healthcare, generative AI is aiding in the discovery of new drugs, personalizing treatment plans, and enhancing medical imaging. By generating realistic simulations and models, AI helps researchers and practitioners explore new avenues in medical science. Business and Marketing: Businesses are employing generative AI to create personalized marketing content, design products, and optimize supply chains. AI-driven tools are enabling companies to innovate faster and more efficiently, providing a competitive edge in the market. Dive into the projects, experiment with different models, and engage with the AI community. By learning, creating, and sharing, you become a part of the vibrant and dynamic landscape of generative AI. The future is filled with opportunities, and this book is your gateway to exploring and contributing to the exciting world of generative AI. Welcome to the journey!

Download or read book Generative AI Business Applications written by David E. Sweenor and published by TinyTechMedia LLC. This book was released on 2024-01-31 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Within the past year, generative AI has broken barriers and transformed how we think about what computers are truly capable of. But, with the marketing hype and generative AI washing of content, it’s increasingly difficult for business leaders and practitioners to go beyond the art of the possible and answer that critical question–how is generative AI actually being used in organizations? With over 70 real-world case studies and applications across 12 different industries and 11 departments, Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies fills a critical knowledge gap for business leaders and practitioners by providing examples of generative AI in action. Diving into the case studies, this TinyTechGuide discusses AI risks, implementation considerations, generative AI operations, AI ethics, and trustworthy AI. The world is transforming before our very eyes. Don’t get left behind—while understanding the powers and perils of generative AI. Full of use cases and real-world applications, this book is designed for business leaders, tech professionals, and IT teams. We provide practical, jargon-free explanations of generative AI's transformative power. Gain a competitive edge in today's marketplace with Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies. Remember, it's not the tech that's tiny, just the book!™

Download or read book GENERATIVE AI WITH PYTHON AND PYTORCH written by JOSEPH. BALI BABCOCK (RAGHAV.) and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Generative AI with Amazon Bedrock written by Shikhar Kwatra and published by Packt Publishing Ltd. This book was released on 2024-07-31 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standards Key Features Learn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist Architects Master the core techniques to develop and deploy several AI applications at scale Go beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learn Explore the generative AI landscape and foundation models in Amazon Bedrock Fine-tune generative models to improve their performance Explore several architecture patterns for different business use cases Gain insights into ethical AI practices, model governance, and risk mitigation strategies Enhance your skills in employing agents to develop intelligence and orchestrate tasks Monitor and understand metrics and Amazon Bedrock model response Explore various industrial use cases and architectures to solve real-world business problems using RAG Stay on top of architectural best practices and industry standards Who this book is for This book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected.

Download or read book The CIO s Guide to Adopting Generative AI Five Keys to Success written by David Sweenor and published by TinyTechMedia LLC. This book was released on 2023-10-24 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a world full of generative AI hoopla, it's easy to get lost in the maze of options and marketing hype. Don't get distracted by the vendor hype; instead, focus on building resilient, high-value platforms that will set you apart from the competition. The CIO’s Guide to Adopting Generative AI: Five Keys to Success fills a critical knowledge gap for CIOs and business leaders by succinctly offering five success factors that need to be met before an organization can successfully incorporate generative AI. To unlock the transformative business value of generative AI, business leaders must: 1) identify enterprise use cases, 2) apply context to large language models (LLMs) using their organization's data, 3) take special precautions to ensure data security and privacy, 4) implement an artificial intelligence (AI) governance framework, and 5) build manageable AI applications for business users. This report provides the keys to unlocking the true potential of generative AI. Full of use cases and real-world applications, this report is designed for business leaders, tech professionals, and IT teams. We provide practical, jargon-free explanations of generative AI's transformative power. Gain a competitive edge in today's marketplace with The CIO’s Guide to Adopting Generative AI: Five Keys to Success. Remember, it's not the tech that's tiny, it's the book!™

Download or read book Hands On Generative AI with Transformers and Diffusion Models written by Omar Sanseviero and published by . This book was released on 2025-01-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains. This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work. Learn the fundamentals of classic and modern generative AI techniques Build and customize models that can generate text, images, and sound Explore trade-offs between training from scratch and using large, pretrained models Create models that can modify images by transferring the style of other images Tweak and bend transformers and diffusion models for creative purposes Train a model that can write text based on your style Deploy models as interactive demos or services