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Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
This book delves into the fascinating world of diffusion models, a powerful tool in generative AI. It equips readers with the knowledge to understand how these models work, explore their applications, and stay informed about future advancements. Part 1: Introduction Chapter 1: Unveils the core concept of diffusion models. It explains how they work by adding noise to data and then learning to reverse the process, ultimately generating new, realistic outputs. The chapter also explores the various applications of diffusion models across diverse fields. Chapter 2: Introduces the broader landscape of generative AI models and compares diffusion models with other popular approaches like VAEs and GANs. This helps readers understand the unique strengths of diffusion models. Part 2: Deep Dive Chapter 3: Dives deeper into the inner workings of diffusion models (optional for those without a strong mathematical background). It explores the concept of probability distributions and other key mathematical concepts that underpin these models. Chapter 4: Explains the diffusion process in detail, including the step-by-step addition of noise and different diffusion model architectures (e.g., U-Net, DDPM). Chapter 5: Explores how diffusion models learn to reverse the noise addition process. It delves into the training techniques and optimization methods used to achieve this remarkable feat. Chapter 6: Explains how to use a trained diffusion model to generate entirely new data. It covers different strategies for initiating the sampling process and controlling the generation by providing prompts or specific styles. Part 3: Applications and Beyond Chapter 7: Showcases how diffusion models can be used for image editing tasks like inpainting (filling in missing parts) and style transfer (applying the style of one image to another). Chapter 8: Pushes the boundaries beyond images. It explores how diffusion models can be adapted to generate different data formats like text, audio, and even 3D structures, opening doors for creative writing, music generation, and scientific research. Chapter 9: Explores cutting-edge research on diffusion models, highlighting their increasing capabilities and potential future directions. This includes improving efficiency and control, making models more interpretable, and addressing ethical considerations. Part 4: Conclusion Chapter 10: Discusses the significant impact of diffusion models on generative AI and various fields. It emphasizes the importance of responsible use and explores ethical considerations like bias, misinformation, and copyright ownership. The chapter concludes with a hopeful outlook on the future of diffusion models and their potential for human-AI collaboration. Overall, this book offers a comprehensive and engaging introduction to diffusion models, empowering readers to not only understand but also leverage this powerful technology for creative exploration and innovation.
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
This book delves into the fascinating world of diffusion models, a powerful tool in generative AI. It equips readers with the knowledge to understand how these models work, explore their applications, and stay informed about future advancements. Part 1: Introduction Chapter 1: Unveils the core concept of diffusion models. It explains how they work by adding noise to data and then learning to reverse the process, ultimately generating new, realistic outputs. The chapter also explores the various applications of diffusion models across diverse fields. Chapter 2: Introduces the broader landscape of generative AI models and compares diffusion models with other popular approaches like VAEs and GANs. This helps readers understand the unique strengths of diffusion models. Part 2: Deep Dive Chapter 3: Dives deeper into the inner workings of diffusion models (optional for those without a strong mathematical background). It explores the concept of probability distributions and other key mathematical concepts that underpin these models. Chapter 4: Explains the diffusion process in detail, including the step-by-step addition of noise and different diffusion model architectures (e.g., U-Net, DDPM). Chapter 5: Explores how diffusion models learn to reverse the noise addition process. It delves into the training techniques and optimization methods used to achieve this remarkable feat. Chapter 6: Explains how to use a trained diffusion model to generate entirely new data. It covers different strategies for initiating the sampling process and controlling the generation by providing prompts or specific styles. Part 3: Applications and Beyond Chapter 7: Showcases how diffusion models can be used for image editing tasks like inpainting (filling in missing parts) and style transfer (applying the style of one image to another). Chapter 8: Pushes the boundaries beyond images. It explores how diffusion models can be adapted to generate different data formats like text, audio, and even 3D structures, opening doors for creative writing, music generation, and scientific research. Chapter 9: Explores cutting-edge research on diffusion models, highlighting their increasing capabilities and potential future directions. This includes improving efficiency and control, making models more interpretable, and addressing ethical considerations. Part 4: Conclusion Chapter 10: Discusses the significant impact of diffusion models on generative AI and various fields. It emphasizes the importance of responsible use and explores ethical considerations like bias, misinformation, and copyright ownership. The chapter concludes with a hopeful outlook on the future of diffusion models and their potential for human-AI collaboration. Overall, this book offers a comprehensive and engaging introduction to diffusion models, empowering readers to not only understand but also leverage this powerful technology for creative exploration and innovation.
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 28
Book Description
This book delves into the fascinating world of diffusion models, a powerful tool in generative AI. It equips readers with the knowledge to understand how these models work, explore their applications, and stay informed about future advancements. Part 1: Introduction Chapter 1: Unveils the core concept of diffusion models. It explains how they work by adding noise to data and then learning to reverse the process, ultimately generating new, realistic outputs. The chapter also explores the various applications of diffusion models across diverse fields. Chapter 2: Introduces the broader landscape of generative AI models and compares diffusion models with other popular approaches like VAEs and GANs. This helps readers understand the unique strengths of diffusion models. Part 2: Deep Dive Chapter 3: Dives deeper into the inner workings of diffusion models (optional for those without a strong mathematical background). It explores the concept of probability distributions and other key mathematical concepts that underpin these models. Chapter 4: Explains the diffusion process in detail, including the step-by-step addition of noise and different diffusion model architectures (e.g., U-Net, DDPM). Chapter 5: Explores how diffusion models learn to reverse the noise addition process. It delves into the training techniques and optimization methods used to achieve this remarkable feat. Chapter 6: Explains how to use a trained diffusion model to generate entirely new data. It covers different strategies for initiating the sampling process and controlling the generation by providing prompts or specific styles. Part 3: Applications and Beyond Chapter 7: Showcases how diffusion models can be used for image editing tasks like inpainting (filling in missing parts) and style transfer (applying the style of one image to another). Chapter 8: Pushes the boundaries beyond images. It explores how diffusion models can be adapted to generate different data formats like text, audio, and even 3D structures, opening doors for creative writing, music generation, and scientific research. Chapter 9: Explores cutting-edge research on diffusion models, highlighting their increasing capabilities and potential future directions. This includes improving efficiency and control, making models more interpretable, and addressing ethical considerations. Part 4: Conclusion Chapter 10: Discusses the significant impact of diffusion models on generative AI and various fields. It emphasizes the importance of responsible use and explores ethical considerations like bias, misinformation, and copyright ownership. The chapter concludes with a hopeful outlook on the future of diffusion models and their potential for human-AI collaboration. Overall, this book offers a comprehensive and engaging introduction to diffusion models, empowering readers to not only understand but also leverage this powerful technology for creative exploration and innovation.
Author: Soon Yau Cheong Publisher: Packt Publishing Ltd ISBN: 1838821104 Category : Computers Languages : en Pages : 306
Book Description
Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch Key FeaturesUnderstand the different architectures for image generation, including autoencoders and GANsBuild models that can edit an image of your face, turn photos into paintings, and generate photorealistic imagesDiscover how you can build deep neural networks with advanced TensorFlow 2.x featuresBook Description The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently. What you will learnTrain on face datasets and use them to explore latent spaces for editing new facesGet to grips with swapping faces with deepfakesPerform style transfer to convert a photo into a paintingBuild and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translationUse iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic imagesBecome well versed in attention generative models such as SAGAN and BigGANGenerate high-resolution photos with Progressive GAN and StyleGANWho this book is for The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You’ll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book.
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 27
Book Description
"AI Design Space Unveiled: Navigating Generative Design with Stable Diffusion, DALL-E 2, and Beyond" is a comprehensive guide that delves into the realm of generative design, offering insights into cutting-edge technologies like Stable Diffusion and DALL-E 2. Authored by leading experts in artificial intelligence and design, this book serves as a roadmap for both beginners and seasoned professionals looking to harness the power of AI in their creative endeavors. The book begins by providing a foundational understanding of generative design principles, laying the groundwork for readers to grasp the concepts and techniques discussed throughout. From there, it explores the revolutionary capabilities of Stable Diffusion, a groundbreaking AI model that excels in generating high-quality images with unprecedented realism and diversity. Readers will learn how Stable Diffusion can be leveraged to expand the boundaries of their creative projects, whether in visual arts, advertising, or product design. A significant portion of the book is dedicated to DALL-E 2, the successor to the original DALL-E model that made waves in the AI community for its ability to generate images from textual descriptions. With DALL-E 2, readers will discover how they can articulate their design ideas through natural language, allowing for a seamless integration of AI into the design workflow. Through practical examples and step-by-step tutorials, readers will learn how to harness the full potential of DALL-E 2 to bring their visions to life. Beyond Stable Diffusion and DALL-E 2, the book also explores other emerging technologies and trends in the field of generative design. From GANs (Generative Adversarial Networks) to reinforcement learning, readers will gain insights into the latest advancements and how they can be applied to their own projects. Throughout the book, emphasis is placed on creativity, innovation, and ethical considerations. Readers will not only learn how to use AI tools effectively but also how to critically evaluate their impact on society and the creative process. "AI Design Space Unveiled" is more than just a guide—it's a roadmap to the future of design, where human ingenuity and artificial intelligence converge to push the boundaries of what's possible. Whether you're a designer, artist, or AI enthusiast, this book will inspire you to explore new horizons in generative design.
Author: Omar Sanseviero Publisher: ISBN: 9781098149246 Category : Computers Languages : en Pages : 0
Book Description
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
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
Practical Generative AI for Data Science: From Theory to Real-World Applications" is a comprehensive guide that bridges the gap between theory and practical implementation of generative AI techniques in the field of data science. This book equips readers with essential knowledge and hands-on skills to effectively harness the power of generative models for diverse applications. Starting with foundational concepts, the book introduces readers to various types of generative models, including Gaussian Mixture Models, Hidden Markov Models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and more. Each model is explained with clear examples, use cases, and case studies drawn from industries such as finance, healthcare, and media. The practical implementation section provides step-by-step tutorials and complete code solutions using popular frameworks like TensorFlow and PyTorch. Readers learn how to build and train models for tasks such as image generation, natural language processing (NLP), anomaly detection, and speech synthesis. Detailed explanations of model architectures, optimization techniques, and evaluation metrics ensure a deep understanding of each concept. Furthermore, the book addresses advanced topics including conditional generative models, sequential generative models like RNNs and Transformers, energy-based models, and diffusion models. These chapters delve into cutting-edge research, emerging trends, and practical applications across various industries. Ethical considerations and regulatory concerns associated with generative AI are also discussed, emphasizing the importance of fairness, transparency, and privacy in model development and deployment. "Practical Generative AI for Data Science" is an indispensable resource for data scientists, machine learning engineers, and researchers looking to leverage generative AI for solving real-world problems. Whether you are new to generative models or seeking to deepen your expertise, this book provides the knowledge and tools needed to succeed in the rapidly evolving field of AI.
Author: Richard Anthony Aragon Publisher: Independently Published ISBN: Category : Languages : en Pages : 0
Book Description
Sure, here is a description for the book titled, "Unpacking Diffusion: A Developer's Guide To Working With Diffusion Models" Unpacking Diffusion: A Developer's Guide To Working With Diffusion Models Diffusion models are a type of generative model that can be used to create realistic images, text, and other creative content. They work by starting with a noisy image or text and then gradually removing the noise, one step at a time. At each step, the model learns to preserve the features of the image or text that are important, while removing the noise. This book is a comprehensive guide to diffusion models for developers. It covers the basics of diffusion models, as well as more advanced topics such as conditional diffusion models and diffusion models for text generation. The book also includes a number of practical tutorials that will help you get started working with diffusion models. Whether you are a beginner or an experienced developer, this book will give you the knowledge and skills you need to work with diffusion models. Here are some of the topics that will be covered in the book: The basics of diffusion models How diffusion models work Different types of diffusion models How to train diffusion models How to use diffusion models to generate images and text Conditional diffusion models Diffusion models for text generation Practical tutorials The book is written in a clear and concise style and is suitable for developers of all levels of experience. It is packed with information and insights that will help you understand and work with diffusion models. If you are interested in learning more about diffusion models, then this book is a must-read.
Author: Jeremy Howard Publisher: O'Reilly Media ISBN: 1492045497 Category : Computers Languages : en Pages : 624
Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
Generative AI Engineering: Techniques, Tools, and Applications is a comprehensive guide that delves into the fascinating world of generative artificial intelligence (AI). This book is designed for AI practitioners, researchers, and enthusiasts who want to understand the principles, methodologies, and practical implementations of generative models. The journey begins with an introduction to generative AI, exploring its history, evolution, key applications, and the ethical considerations that come with this powerful technology. The fundamentals of machine learning and deep learning are covered next, laying a solid foundation with an overview of neural networks, their architectures, and common algorithms. The book then delves into the heart of generative AI with detailed chapters on various generative models. Variational Autoencoders (VAEs) are explored first, from their mathematical foundations to implementation and real-world applications. This is followed by a deep dive into Generative Adversarial Networks (GANs), examining their architecture, training techniques, and different variants like DCGAN and StyleGAN. Autoregressive models, including RNNs, LSTMs, and Transformers, are discussed next, highlighting their training and optimization techniques. Diffusion models, with their unique approach to generating data through noise reduction, are also thoroughly explained. Advanced topics in generative AI are explored, including transfer learning, few-shot and zero-shot learning, and the integration of reinforcement learning. Hybrid models that combine different generative approaches are also discussed. Practical implementation guides provide step-by-step instructions for building, training, and deploying generative models using popular tools and libraries like TensorFlow, PyTorch, and Keras. Evaluation and metrics chapters offer insights into assessing the performance of generative models using qualitative and quantitative measures. The book also covers diverse applications and case studies, showcasing the impact of generative AI in fields such as image and text generation, music and audio synthesis, video creation, healthcare, and the creative industries. Finally, the book addresses current challenges in generative AI, including ethical and societal impacts, and outlines future research directions that promise to drive the field forward.
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
Generative AI in Theory: Practical Applications and Concepts" delves into the fundamentals and applications of generative artificial intelligence (AI), offering a comprehensive exploration suitable for both beginners and seasoned professionals. This book is a definitive guide that demystifies complex AI techniques and showcases their practical implementations across various domains. Starting with foundational concepts, the book covers essential topics such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models. Readers will gain insights into how these models generate new data instances, create realistic images, and even compose music. The theoretical underpinnings are explained with clarity, accompanied by hands-on examples and tutorials using popular frameworks like TensorFlow and PyTorch. Moving to advanced topics, the book explores Energy-Based Models including Boltzmann Machines and Contrastive Divergence, providing practical insights into their applications in recommendation systems and anomaly detection. Neural Ordinary Differential Equations are introduced as a powerful tool for continuous-time sequence modeling, offering real-world applications in time-series forecasting and simulations. Flow-Based Models like Normalizing Flows and Diffusion Models are detailed for their capabilities in high-quality image generation and text completion tasks. Case studies in natural language processing, computer vision, and audio generation illustrate how these techniques are transforming industries. The ethical and societal implications of generative AI are carefully examined, addressing concerns such as bias, privacy, and economic impacts. The book concludes with a forward-looking perspective on emerging trends and unresolved challenges, preparing readers for the future of AI innovation. "Generative AI in Theory" is an essential resource for AI enthusiasts, researchers, and practitioners seeking a deeper understanding of cutting-edge AI technologies and their practical applications across diverse fields.
Author: Anand Vemula
Author: Anand Vemula
Author: Anand Vemula
Author: Soon Yau Cheong
Author: Anand Vemula
Author: Omar Sanseviero
Author: Anand Vemula
Author: Richard Anthony Aragon
Author: Jeremy Howard
Author: Anand Vemula
Author: Anand Vemula