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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: 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: 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: Erik R. Ranschaert Publisher: Springer ISBN: 3319948784 Category : Medical Languages : en Pages : 369
Book Description
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Author: Perry Carpenter Publisher: John Wiley & Sons ISBN: 1394299893 Category : Computers Languages : en Pages : 235
Book Description
Learn to navigate a world of deepfakes, phishing attacks, and other cybersecurity threats emanating from generative artificial intelligence In an era where artificial intelligence can create content indistinguishable from reality, how do we separate truth from fiction? In FAIK: A Practical Guide to Living in a World of Deepfakes, Disinformation, and AI-Generated Deceptions, cybersecurity and deception expert Perry Carpenter unveils the hidden dangers of generative artificial intelligence, showing you how to use these technologies safely while protecting yourself and others from cyber scams and threats. This book provides a crucial understanding of the potential risks associated with generative AI, like ChatGPT, Claude, and Gemini, offering effective strategies to avoid falling victim to their more sinister uses. This isn't just another book about technology – it's your survival guide to the digital jungle. Carpenter takes you on an insightful journey through the "Exploitation Zone," where rapid technological advancements outpace our ability to adapt, creating fertile ground for deception. Explore the mechanics behind deepfakes, disinformation, and other cognitive security threats. Discover how cybercriminals can leverage even the most trusted AI systems to create and spread synthetic media and use it for malicious purposes. At its core, FAIK is an empowering exposé in which Carpenter effectively weaves together engaging narratives and practical insights, all aimed to equip you with the knowledge to recognize and counter advanced tactics with practical media literacy skills and a deep understanding of social engineering. You will: Learn to think like a hacker to better defend against digital threats. Gain practical skills to identify and defend against AI-driven scams. Develop your toolkit to safely navigate the "Exploitation Zone." See how bad actors exploit fundamental aspects of generative AI to create weapons grade deceptions. Develop practical skills to identify and resist emotional manipulation in digital content. Most importantly, this is ultimately an optimistic book as it predicts a powerful and positive outcome as a period of cooperation, something now inconceivable, develops as it always does during crises and the future is enhanced by amazing new technologies and fabulous opportunities on the near horizon. Written by an expert, yet accessible to everyone, FAIK is an indispensable resource for anyone who uses technology and wants to stay secure in the evolving digital landscape. This book not only prepares you to face the onslaught of digital deceptions and AI-generated threats, but also teaches you to think like a hacker to better defend against them.
Author: Shoji Makino Publisher: Springer Science & Business Media ISBN: 9783540240396 Category : Hearing Languages : en Pages : 432
Book Description
We live in a noisy world! In all applications (telecommunications, hands-free communications, recording, human-machine interfaces, etc.) that require at least one microphone, the signal of interest is usually contaminated by noise and reverberation. As a result, the microphone signal has to be "cleaned" with digital signal processing tools before it is played out, transmitted, or stored. This book is about speech enhancement. Different well-known and state-of-the-art methods for noise reduction, with one or multiple microphones, are discussed. By speech enhancement, we mean not only noise reduction but also dereverberation and separation of independent signals. These topics are also covered in this book. However, the general emphasis is on noise reduction because of the large number of applications that can benefit from this technology. The goal of this book is to provide a strong reference for researchers, engineers, and graduate students who are interested in the problem of signal and speech enhancement. To do so, we invited well-known experts to contribute chapters covering the state of the art in this focused field. TOC:Introduction.- Study of the Wiener Filter for Noise Reduction.- Statistical Methods for the Enhancement of Noisy Speech.- Single- und Multi-Microphone Spectral Amplitude Estimation Using a Super-Gaussian Speech Model.- From Volatility Modeling of Financial Time-Series to Stochastic Modeling and Enhancement of Speech Signals.- Single-Microphone Noise Suppression for 3G Handsets Based on Weighted Noise Estimation.- Signal Subspace Techniques for Speech Enhancement.- Speech Enhancement: Application of the Kalman Filter in the Estimate-Maximize (EM) Framework.- Speech Distortion Weighted Multichannel Wiener Filtering Techniques for Noise Reduction.- Adpative Microphone Arrays Employing Spatial Quadratic Soft Constraints and Spectral Shaping.- Single-Microphone Blind Dereverberation.- Separation and Dereverberation of Speech Signals with Multiple Microphones.- Frequency-Domain Blind Source Separation.- Subband Based Blind Source Separation.- Real-Time Blind Source Separation for Moving Speech Signals.- Separation of Speech by Computational Auditory Scene Analysis
Author: David Foster Publisher: "O'Reilly Media, Inc." ISBN: 109813415X Category : Computers Languages : en Pages : 456
Book Description
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
Author: Kumar, Raghvendra Publisher: IGI Global ISBN: Category : Computers Languages : en Pages : 360
Book Description
Integrating generative artificial intelligence (AI) into art, design, and media presents a double-edged sword. While it offers unprecedented creative possibilities, it raises ethical concerns, challenges traditional workflows, and requires careful regulation. As AI becomes more prevalent in these fields, there is a pressing need for a comprehensive resource that explores the technology's potential and navigates the complex landscape of its implications. The Pioneering Applications of Generative AI is a pioneering book that addresses these challenges head-on. It provides a deep dive into the evolution, ethical considerations, core technologies, and creative applications of generative AI, offering readers a thorough understanding of this transformative technology. Researchers, academicians, scientists, and research scholars will find this book invaluable in navigating the complexities of generative AI in art, design, and media. With its focus on ethical and responsible AI and discussions on regulatory frameworks, the book equips readers with the knowledge and tools needed to harness the full potential of generative AI while ensuring its responsible and ethical use.
Author: James Phoenix Publisher: "O'Reilly Media, Inc." ISBN: 1098153391 Category : Languages : en Pages : 430
Book Description
Large language models (LLMs) and diffusion models such as ChatGPT and Stable Diffusion have unprecedented potential. Because they have been trained on all the public text and images on the internet, they can make useful contributions to a wide variety of tasks. And with the barrier to entry greatly reduced today, practically any developer can harness LLMs and diffusion models to tackle problems previously unsuitable for automation. With this book, you'll gain a solid foundation in generative AI, including how to apply these models in practice. When first integrating LLMs and diffusion models into their workflows, most developers struggle to coax reliable enough results from them to use in automated systems. Authors James Phoenix and Mike Taylor show you how a set of principles called prompt engineering can enable you to work effectively with AI. Learn how to empower AI to work for you. This book explains: The structure of the interaction chain of your program's AI model and the fine-grained steps in between How AI model requests arise from transforming the application problem into a document completion problem in the model training domain The influence of LLM and diffusion model architecture—and how to best interact with it How these principles apply in practice in the domains of natural language processing, text and image generation, and code
Author: Anand Vemula
Author: Anand Vemula
Author: Soon Yau Cheong
Author: Jeremy Howard
Author: Erik R. Ranschaert
Author: Zhihan Lyu
Author: Perry Carpenter
Author: Shoji Makino
Author: David Foster
Author: Kumar, Raghvendra
Author: James Phoenix