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• Table of Contents An Introduction to Generative AI: "Drawing" Data from Models Setting Up a TensorFlow Lab Building Blocks of Deep Neural Networks Teaching Networks to Generate Digits Painting Pictures with Neural Networks Using VAEs Image Generation with GANs Style Transfer with GANs Deepfakes with GANs The Rise of Methods for Text Generation NLP 2
• .0: Using Transformers to Generate Text Composing Music with Generative Models Play Video Games with Generative AI: GAIL Emerging Applications in Generative AI.
• (source: Nielsen Book Data)

Understand the theory behind deep generative models and experiment with practical examples Key Features Build a solid understanding of the inner workings of generative models Experiment with practical TensorFlow 2.x implementations of state-of-the-art models Explore a wide range of current and emerging use cases for deep generative AI Book DescriptionDeep generative models are powerful tools that rival human creative capabilities. In this book, you'll discover how these models emerged, from restricted Boltzmann machines and deep belief networks to VAEs, GANs, and beyond. You'll develop a foundational understanding of generative AI and learn how to implement models yourself in TensorFlow, supported by references to seminal and current research. After getting to grips with the fundamentals of deep neural networks, you'll set up a scalable code lab in the cloud and begin to explore the huge breadth of potential use cases for generative models. You'll look at Open AI's news generator, networks for style transfer and deepfakes, synergy with reinforcement learning, and more. As you progress, you'll recreate the code that makes these possible, piecing together TensorFlow layers, utility functions, and training loops to uncover links between the different modes of generation. By the end of this book, you will have acquired the knowledge to create and implement your own generative AI models. What you will learn Implement paired and unpaired style transfer with networks like StyleGAN Use facial landmarks, autoencoders, and pix2pix GAN to create deepfakes Build several text generation pipelines based on LSTMs, BERT, and GPT-2, learning how attention and transformers changed the NLP landscape Compose music using hands-on LSTM models, simple GANs, and the intricate MuseGAN Train a deep learning agent to move through a simulated physical environment Discover emerging applications of generative AI, such as folding proteins and creating videos from images Who this book is forThis book will appeal to Python programmers, seasoned modelers, and machine learning engineers who are keen to learn about the creation and implementation of generative models. To make the most out of this book, you should have a basic familiarity with probability theory, linear algebra, and deep learning.
(source: Nielsen Book Data)

Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book * Master open source Python tools to build sophisticated predictive models * Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide * Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn * Gain an insight into components and design decisions for an analytical application * Master the use Python notebooks for exploratory data analysis and rapid prototyping * Get to grips with applying regression, classification, clustering, and deep learning algorithms * Discover the advanced methods to analyze structured and unstructured data * Find out how to deploy a machine learning model in a production environment * Visualize the performance of models and the insights they produce * Scale your solutions as your data grows using Python * Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.
(source: Nielsen Book Data)

Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book * Master open source Python tools to build sophisticated predictive models * Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide * Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn * Gain an insight into components and design decisions for an analytical application * Master the use Python notebooks for exploratory data analysis and rapid prototyping * Get to grips with applying regression, classification, clustering, and deep learning algorithms * Discover the advanced methods to analyze structured and unstructured data * Find out how to deploy a machine learning model in a production environment * Visualize the performance of models and the insights they produce * Scale your solutions as your data grows using Python * Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.
(source: Nielsen Book Data)

Gain practical insights by exploiting data in your business to build advanced predictive modeling applications About This Book * A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices * Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering * Master open source Python tools to build sophisticated predictive models Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you. What You Will Learn * Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries * Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy * Master the use of Python notebooks for exploratory data analysis and rapid prototyping * Get to grips with applying regression, classification, clustering, and deep learning algorithms * Discover advanced methods to analyze structured and unstructured data * Visualize the performance of models and the insights they produce * Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python Style and approach This course aims to create a smooth learning path that will teach you how to effectively perform predictive analytics using Python. Through this comprehensive course, you'll learn the basics of predictive analytics and progress to predictive modeling in the modern world.
(source: Nielsen Book Data)

• Foreword: Writing About War Is Writing About Peace, by Bao Ninh Acknowledgments Introduction: A Note on the Selection and Translation of Stories, by Quan Manh Ha and Joseph Babcock
• 1. Unsung Hero, by Nguyen Van Tho
• 2. White Clouds Flying, by Bao Ninh
• 3. Louse Crab Season, by Mai Tien Nghi
• 4. Birds in Formation, by Nguyen Ngoc Tu
• 5. A Crescent Moon in the Woods, by Nguyen Minh Chau
• 6. Ms. Thoai, by Hanh Le
• 7. The Corporal, by Nguyen Trong Luan
• 8. Red Apples, by Vuong Tam
• 9. The Most Beautiful Girl in the Village, by Ta Duy Anh
• 10. Brother, When Will You Come Home?, by Truong Van Ngoc
• 11. War, by Thai Ba Tan
• 12. The Chau River Pier, by Suong Nguyet Minh
• 13. Storms, by Nguyen Thi Mai Phuong
• 14. They Became Men, by Pham Ngoc Tien
• 15. An American Service Hamlet, by Nguyen Thi Thu Tran
• 16. Love and War, by Nguyen Ngoc Thuan
• 17. The Person Coming from the Woods, by Nguyen Thi Am
• 18. Out of the Laughing Woods, by Vo Thi Hao
• 19. The Sorrow Wasn't Just Ours, by Luong Liem
• 20. A Moral Murderer, by Lai Van Long Permissions and Acknowledgments.
• (source: Nielsen Book Data)

In this anthology, Vietnamese writers describe their experience of what they call the American War and its lasting legacy through the lens of their own vital artistic visions. A North Vietnamese soldier forms a bond with an abandoned puppy. Cousins find their lives upended by the revelation that their fathers fought on opposite sides of the war. Two lonely veterans in Hanoi meet years after the war has ended through a newspaper dating service. A psychic assists the search for the body of a long-vanished soldier. The father of a girl suffering from dioxin poisoning struggles with corrupt local officials. The twenty short stories collected in Other Moons range from the intensely personal to narratives that deal with larger questions of remembrance, trauma, and healing. By a diverse set of authors, including many veterans, they span styles from social realism to tales of the fantastic. Yet whether describing the effects of Agent Orange exposure or telling ghost stories, all speak to the unresolved legacy of a conflict that still haunts Vietnam. Among the most widely anthologized and popular pieces of short fiction about the war in Vietnam, these works appear here for the first time in English. Other Moons offers Anglophone audiences an unparalleled opportunity to experience how the Vietnamese think and write about the conflict that consumed their country from 1954 to 1975-a perspective still largely missing from American narratives.
(source: Nielsen Book Data)

• Foreword: Writing About War Is Writing About Peace, by Bao Ninh Acknowledgments Introduction: A Note on the Selection and Translation of Stories, by Quan Manh Ha and Joseph Babcock
• 1. Unsung Hero, by Nguyen Van Tho
• 2. White Clouds Flying, by Bao Ninh
• 3. Louse Crab Season, by Mai Tien Nghi
• 4. Birds in Formation, by Nguyen Ngoc Tu
• 5. A Crescent Moon in the Woods, by Nguyen Minh Chau
• 6. Ms. Thoai, by Hanh Le
• 7. The Corporal, by Nguyen Trong Luan
• 8. Red Apples, by Vuong Tam
• 9. The Most Beautiful Girl in the Village, by Ta Duy Anh
• 10. Brother, When Will You Come Home?, by Truong Van Ngoc
• 11. War, by Thai Ba Tan
• 12. The Chau River Pier, by Suong Nguyet Minh
• 13. Storms, by Nguyen Thi Mai Phuong
• 14. They Became Men, by Pham Ngoc Tien
• 15. An American Service Hamlet, by Nguyen Thi Thu Tran
• 16. Love and War, by Nguyen Ngoc Thuan
• 17. The Person Coming from the Woods, by Nguyen Thi Am
• 18. Out of the Laughing Woods, by Vo Thi Hao
• 19. The Sorrow Wasn't Just Ours, by Luong Liem
• 20. A Moral Murderer, by Lai Van Long Permissions and Acknowledgments.
• (source: Nielsen Book Data)

In this anthology, Vietnamese writers describe their experience of what they call the American War and its lasting legacy through the lens of their own vital artistic visions. A North Vietnamese soldier forms a bond with an abandoned puppy. Cousins find their lives upended by the revelation that their fathers fought on opposite sides of the war. Two lonely veterans in Hanoi meet years after the war has ended through a newspaper dating service. A psychic assists the search for the body of a long-vanished soldier. The father of a girl suffering from dioxin poisoning struggles with corrupt local officials. The twenty short stories collected in Other Moons range from the intensely personal to narratives that deal with larger questions of remembrance, trauma, and healing. By a diverse set of authors, including many veterans, they span styles from social realism to tales of the fantastic. Yet whether describing the effects of Agent Orange exposure or telling ghost stories, all speak to the unresolved legacy of a conflict that still haunts Vietnam. Among the most widely anthologized and popular pieces of short fiction about the war in Vietnam, these works appear here for the first time in English. Other Moons offers Anglophone audiences an unparalleled opportunity to experience how the Vietnamese think and write about the conflict that consumed their country from 1954 to 1975-a perspective still largely missing from American narratives.
(source: Nielsen Book Data)