Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized

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.

An indispensable look at the next frontier of technological advancement and its impact on our world Generative AI is rewriting the rulebook with its seemingly endless capabilities, from crafting intricate industrial designs, writing computer code, and producing mesmerizing synthetic voices to composing enchanting music and innovating genetic breakthroughs. In Generative AI in Practice, renowned futurist Bernard Marr offers readers a deep dive into the captivating universe of GenAI. This comprehensive guide introduces you to the basics of this groundbreaking technology and outlines the profound impact that GenAI will have on business and society. Professionals, technophiles, and anyone with an interest in the future will need to understand how GenAI is set to redefine jobs, revolutionize business, and question the foundations everything we do. In this book, Marr sheds light on the most innovative real-world GenAI applications through practical examples, describing how they are moulding industries like retail, healthcare, education, finance, and beyond. You'll enjoy a captivating discussion of innovations in media and entertainment, seismic shifts in advertising, and the future trajectory of GenAI. You will: Navigate the complex landscapes of risks and challenges posed by Generative AI Delve into the revolutionary transformation of the job market in the age of GenAI Understand AI's transformative impact on education, healthcare, and retail Explore the boundless potentials in media, design, banking, coding, and even the legal arena Ideal for professionals, technophiles, and anyone eager to understand the next big thing in technology, Generative AI In Practice will equip readers with insights on how to implement GenAI, how GenAI is different to traditional AI, and a comprehensive list of generative AI tools available today.

In an era marked by the rapid integration of Artificial Intelligence (AI) into our lives, the discourse surrounding its implications has intensified. The Role of Generative AI in the Communication Classroom is a pioneering book that delves into the multifaceted dimensions of AI, specifically focusing on OpenAI's revolutionary Chat Generative Pre-Trained Transformer (Chat GPT) and its profound influence on the landscape of communication education. This book navigates the intersection of technology, education, and ethics, shedding light on the imperative need for a collaborative approach to shape AI's evolution. AI's potential to reshape industries and human roles is undeniable. Rooted in the intricate workings of AI and its hallmark, Chat GPT, this book meticulously dissects the dynamic relationship between humans and machines. The discourse extends beyond technology and into the realm of education, asserting that the power to mold AI's trajectory cannot rest solely in the hands of developers. While revealing AI's transformative potential in the communication classroom, the book conscientiously explores ethical concerns and biases, fostering a balanced approach to its integration. This book is instrumental to the ongoing discourse on AI's role in education. The call for ethical considerations, inclusivity, and regulation serves as a guiding compass for educators, students, developers, and policymakers alike. The book ensures a holistic perspective on AI's integration by addressing privacy, citation, voice ownership, and overall digital ethics.

Generative Artificial Intelligence (AI), an ever-evolving technology, holds immense promise across various industries, from healthcare to content generation. However, its rapid advancement has also given rise to profound ethical concerns. Illicit black-market industries exploit generative AI for counterfeit imagery, and in educational settings, biases and misinformation perpetuate. These issues underscore the need to grapple with the risks accompanying generative AI integration. Exploring the Ethical Implications of Generative AI emerges as a wellspring of insight for discerning academic scholars. It sets the stage by acknowledging generative AI's multifaceted potential and its capacity to reshape industries. The book addresses these complex ethical concerns, offering a comprehensive analysis and providing a roadmap for responsible AI development and usage. Its intended audience spans business leaders, policymakers, scholars, and individuals passionate about the ethical dimensions of AI.

Melanie Mitchell separates science fact from science fiction in this sweeping examination of the current state of AI and how it is remaking our world No recent scientific enterprise has proved as alluring, terrifying, and filled with extravagant promise and frustrating setbacks as artificial intelligence. The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it. In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go. Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.