Download or read book Deep Learning in Generative AI From Fundamentals to Cutting Edge Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an in-depth exploration of the foundational concepts, advanced techniques, and practical applications of generative AI, all powered by deep learning. The journey begins with a solid introduction to generative models, explaining their significance in AI and how they differ from discriminative models. It then covers the foundational elements of deep learning, including neural networks, backpropagation, activation functions, and optimization methods, laying the groundwork for understanding complex generative architectures. The book progresses to detailed discussions on various generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. Each model is presented with its mathematical foundations, architecture, and step-by-step coding tutorials, making it accessible to both beginners and advanced practitioners. Real-world applications of these models are explored in depth, showcasing how generative AI is transforming industries like healthcare, finance, and creative arts. The book also addresses the challenges associated with training generative models, offering practical solutions and optimization techniques. Ethical considerations are a critical component, with dedicated sections on bias in generative models, deepfakes, and the implications of AI-generated content on intellectual property. The book concludes with a forward-looking discussion on future trends in generative AI, including the integration of AI with quantum computing and its role in promoting sustainability. With a balanced mix of theory, hands-on exercises, case studies, and practical examples, this book equips readers with the knowledge and tools to implement generative AI models in real-world scenarios, making it an essential resource for AI enthusiasts, researchers, and professionals.

Download or read book Generative AI and Deep Learning written by Anand Vemula and published by Independently Published. This book was released on 2024-05-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Generative AI and Deep Learning: From Fundamentals to Advanced Applications" is a comprehensive guide that explores the exciting field of artificial intelligence (AI) and deep learning. Written for both beginners and seasoned professionals, this book delves into the foundational concepts of generative AI and deep learning architectures, including neural networks basics, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. The book starts with an overview of generative models, explaining their significance in generating new data samples and their various applications across industries. It covers popular generative models like autoencoders, restricted Boltzmann machines (RBMs), and deep belief networks (DBNs), providing insights into their workings and real-world use cases. Moving beyond the basics, the book explores advanced topics in generative AI, such as reinforcement learning integration and its applications in natural language processing (NLP). Readers will learn about cutting-edge techniques like transformer models, including BERT and GPT, and how they revolutionize language understanding and generation tasks. Throughout the book, ethical considerations and challenges in generative AI are highlighted, emphasizing the importance of fairness, transparency, and security in AI development. Real-world case studies showcase successful implementations of generative AI across diverse domains, from healthcare and finance to art and entertainment. Practical guidance is provided on building and deploying generative models, including model training, evaluation, and optimization strategies. The book also explores popular tools and frameworks like TensorFlow, PyTorch, and OpenAI GPT, empowering readers to harness the full potential of generative AI technology. With insights into emerging trends and future directions, "Generative AI and Deep Learning" offers a holistic view of the field, inspiring readers to explore new possibilities and contribute to the advancement of AI for the betterment of society. Whether you're a student, researcher, or industry professional, this book is your essential companion on the journey through the exciting world of generative AI and deep learning. Keywords: Generative AI, Deep Learning, Neural Networks, Autoencoders, Reinforcement Learning, Natural Language Processing, Ethics, Case Studies, Tools and Frameworks, Future Directions.

Download or read book Mastering AI and Generative AI From Learning Fundamentals to Advanced Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive guide dives into the fascinating world of Artificial Intelligence (AI) and its cutting-edge subfield, Generative AI. Designed for beginners and enthusiasts alike, it equips you with the knowledge and skills to navigate the complexities of machine learning and unlock the power of AI for advanced applications. Building a Strong Foundation The journey begins with mastering the fundamentals. You'll explore the different approaches to AI, delve into the history of this revolutionary field, and gain a solid understanding of various subfields like Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Delving into Machine Learning Machine learning, the core of AI's learning ability, takes center stage. You'll grasp the difference between supervised and unsupervised learning paradigms, discover popular algorithms like decision trees and neural networks, and learn the importance of data preparation for optimal model performance. Evaluation metrics become your tools to measure how effectively your models are learning. Unveiling the Power of Deep Learning Get ready to explore the intricate world of Deep Learning, a powerful subset of machine learning inspired by the human brain. Demystify neural networks, the building blocks of deep learning, and dive into specialized architectures like Convolutional Neural Networks (CNNs) for image recognition and Recurrent Neural Networks (RNNs) for handling sequential data. Deep learning frameworks become your allies, simplifying the process of building and deploying complex deep learning models. The Art of Machine Creation: Generative AI The book then shifts its focus to the transformative realm of Generative AI. Here, machines not only learn but create entirely new data. Explore different types of generative models, from autoregressive models to variational autoencoders, and witness their applications in text generation, image synthesis, and even music creation. A Deep Dive into Generative Adversarial Networks (GANs) Among generative models, Generative Adversarial Networks (GANs) have captured the imagination of researchers and the public alike. This chapter delves into the intriguing concept of GANs, where a generator model continuously strives to create realistic data while a discriminator model acts as a critic, ensuring the generated data is indistinguishable from real data. You'll explore the training process, the challenges of taming GANs, and best practices for achieving optimal results. Advanced Applications Across Domains The book then showcases the transformative potential of Generative AI across various domains. Witness the power of text generation with RNNs, explore the ethical considerations surrounding deepfakes, and discover how chatbots are revolutionizing communication. In the visual realm, delve into Deep Dream and Neural Style Transfer algorithms, and witness the creation of realistic images and videos with cutting-edge generative models. Mastering AI and Generative AI empowers you to not only understand these revolutionary technologies but also leverage them for advanced applications. As you embark on this journey, be prepared to unlock the boundless potential of machine creation and shape the future of AI.

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2022-06-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Generative AI Research written by Anand Vemula and published by Independently Published. This book was released on 2024-06-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI Research: Mastering Foundations, Models, and Practical Applications is a comprehensive guide that delves into the fascinating world of generative artificial intelligence. This book is meticulously designed for researchers, practitioners, and enthusiasts who are keen to explore and harness the power of generative AI. Starting with an introduction to AI and machine learning, the book provides a solid foundation by explaining key concepts and the historical development of generative models. It dives into the mathematical and statistical underpinnings essential for understanding generative AI, followed by a thorough exploration of machine learning and deep learning fundamentals. The book categorizes and examines various types of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), autoregressive models, and flow-based models. Each section covers the architecture, applications, and challenges associated with these models, supplemented with real-world examples and use cases. Readers will find detailed tutorials with complete solutions, enabling hands-on learning and practical implementation of concepts. For instance, the section on GANs provides step-by-step guidance on building and training GANs, addressing common pitfalls and optimization strategies. Moreover, the book highlights diverse applications of generative AI across various domains such as image generation, text creation, music synthesis, and video editing. Advanced topics like conditional generative models, multimodal generative models, and few-shot learning are also discussed, offering insights into cutting-edge research and developments. Practical exercises with complete solutions are included to reinforce learning and provide a robust understanding of how to apply generative AI techniques in real-world scenarios. The book also addresses the evaluation metrics for generative models, ensuring readers can effectively measure the performance of their models. Generative AI Research: Mastering Foundations, Models, and Practical Applications is an essential resource that bridges the gap between theory and practice, equipping readers with the knowledge and skills needed to excel in the dynamic field of generative AI.

Download or read book Generative Deep Learning with Python written by Cuantum Technologies LLC and published by Packt Publishing Ltd. This book was released on 2024-06-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the world of Generative Deep Learning with Python, mastering GANs, VAEs, & autoregressive models through projects & advanced topics. Gain practical skills & theoretical knowledge to create groundbreaking AI applications. Key Features Comprehensive coverage of deep learning and generative models. In-depth exploration of GANs, VAEs, & autoregressive models & advanced topics in generative AI. Practical coding exercises & interactive assignments to build your own generative models. Book DescriptionGenerative Deep Learning with Python opens the door to the fascinating world of AI where machines create. This course begins with an introduction to deep learning, establishing the essential concepts and techniques. You will then delve into generative models, exploring their theoretical foundations and practical applications. As you progress, you will gain a deep understanding of Generative Adversarial Networks (GANs), learning how they function and how to implement them for tasks like face generation. The course's hands-on projects, such as creating GANs for face generation and using Variational Autoencoders (VAEs) for handwritten digit generation, provide practical experience that reinforces your learning. You'll also explore autoregressive models for text generation, allowing you to see the versatility of generative models across different types of data. Advanced topics will prepare you for cutting-edge developments in the field. Throughout your journey, you will gain insights into the future landscape of generative deep learning, equipping you with the skills to innovate and lead in this rapidly evolving field. By the end of the course, you will have a solid foundation in generative deep learning and be ready to apply these techniques to real-world challenges, driving advancements in AI and machine learning.What you will learn Develop a detailed understanding of deep learning fundamentals Implement and train Generative Adversarial Networks (GANs) Create & utilize Variational Autoencoders for data generation Apply autoregressive models for text generation Explore advanced topics & stay ahead in the field of generative AI Analyze and optimize the performance of generative models Who this book is for This course is designed for technical professionals, data scientists, and AI enthusiasts who have a foundational understanding of deep learning and Python programming. It is ideal for those looking to deepen their expertise in generative models and apply these techniques to innovative projects. Prior experience with neural networks and machine learning concepts is recommended to maximize the learning experience. Additionally, research professionals and advanced practitioners in AI seeking to explore generative deep learning applications will find this course highly beneficial.

Download or read book AI Unraveled Master GPT x Gemini Generative AI LLMs Prompt Engineering A simplified Guide For Everyday Users written by Etienne Noumen and published by Etienne Noumen. This book was released on with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive into the revolutionary world of Artificial Intelligence with 'AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence'. This comprehensive guide is your portal to understanding AI's most intricate concepts and cutting-edge developments. Whether you're a curious beginner or an AI enthusiast, this book is tailored to unveil the complexities of AI in a simple, accessible manner. What's Inside: Fundamental AI Concepts: Journey through the basics of AI, machine learning, deep learning, and neural networks. AI in Action: Explore how AI is reshaping industries and society, diving into its applications in computer vision, natural language processing, and beyond. Ethical AI: Tackle critical issues like AI ethics and bias, understanding the moral implications of AI advancements. Industry Insights: Gain insights into how AI is revolutionizing industries and impacting our daily lives. The Future of AI: Forecast the exciting possibilities and challenges that lie ahead in the AI landscape. Special Focus on Generative AI & LLMs: Latest AI Trends: Stay updated with the latest in AI, including ChatGPT, Google Bard, GPT-4, Gemini, and more. Interactive Quizzes: Test your knowledge with engaging quizzes on Generative AI and Large Language Models (LLMs). Practical Guides: Master GPT-4 with a simplified guide, delve into advanced prompt engineering, and explore the nuances of temperature settings in AI. Real-World Applications: Learn how to leverage AI in various sectors, from healthcare to cybersecurity, and even explore its potential in areas like aging research and brain implants. For the AI Enthusiast: Prompt Engineering: Uncover secrets to crafting effective prompts for ChatGPT/Google Bard. AI Career Insights: Explore lucrative career paths in AI, including roles like AI Prompt Engineers. AI Investment Guide: Navigate the world of AI stocks and investment opportunities. Your Guide to Navigating AI: Do-It-Yourself Tutorials: From building custom ChatGPT applications to running LLMs locally, this book offers step-by-step guides. AI for Everyday Use: Learn how AI can assist in weight loss, social media, and more. 'AI Unraveled' is more than just a book; it's a resource for anyone looking to grasp the complexities of AI and its impact on our world. Get ready to embark on an enlightening journey into the realm of Artificial Intelligence!" More Topics Covered: Artificial Intelligence, Machine Learning, Deep Learning, NLP, AI Ethics, Robotics, Cognitive Computing, ChatGPT, OpenAI, Google Bard, Generative AI, LLMs, AI in Healthcare, AI Investments, and much more. GPT-4 vs Gemini: Pros and Cons Mastering GPT-4: Simplified Guide For everyday Users Advance Prompt Engineering Techniques: [Single Prompt Technique, Zero-Shot and Few-Shot, Zero-Shot and Few-Shot, Generated Knowledge Prompting, EmotionPrompt, Chain of Density (CoD), Chain of Thought (CoT), Validation of LLMs Responses, Chain of Verification (CoVe), Agents - The Frontier of Prompt Engineering, Prompt Chaining vs Agents, Tree of Thought (ToT), ReAct (Reasoning + Act), ReWOO (Reasoning WithOut Observation), Reflexion and Self-Reflection, Guardrails, RAIL (Reliable AI Markup Language), Guardrails AI, NeMo Guardrails] Understanding Temperature in GPT-4: A Guide to AI Probability and Creativity Retrieval-Augmented Generation (RAG) model in the context of Large Language Models (LLMs) like GPT-4 Prompt Ideas for ChatGPT/Google Bard How to Run ChatGPT-like LLMs Locally on Your Computer in 3 Easy Steps ChatGPT Custom Instructions Settings for Power Users Examples of bad and good ChatGPT prompts Top 5 Beginner Mistakes in Prompt Engineering Use ChatGPT like a PRO Prompt template for learning any skill Prompt Engineering for ChatGPT The Future of LLMs in Search What is Explainable AI? Which industries are meant for XAI? ChatGPT Best Tips, Cheat Sheet LLMs Utilize Vector DB for Data Storage The Limitation Technique in Prompt Responses Use ChatGPT to learn new subjects Prompts to proofread anything Topics: Artificial Intelligence Education Machine Learning Deep Learning Reinforcement Learning Neural networks Data science AI ethics Deepmind Robotics Natural language processing Intelligent agents Cognitive computing AI Apps AI impact AI Tech ChatGPT Open AI Safe AI Generative AI Discriminative AI Sam Altman Google Bard NVDIA Large Language Models (LLMs) PALM GPT Explainable AI GPUs AI Stocks AI Podcast Q* AI Certification AI Quiz RAG How to access the AI Unraveled print and audiobook: Amazon print book: https://amzn.to/3xvCfWR Audible at Amazon : https://www.audible.com/pd/B0BXMJ7FK5/?source_code=AUDFPWS0223189MWT-BK-ACX0-343437&ref=acx_bty_BK_ACX0_343437_rh_us (Use Promo code: 37YT3B5UYUYZW) Audiobook at Google: https://play.google.com/store/audiobooks/details?id=AQAAAEAihFTEZM Amazon eBook: https://amzn.to/3KbshkO Google eBook: https://play.google.com/store/books/details?id=oySuEAAAQBAJ Apple eBook: http://books.apple.com/us/book/id6445730691

Download or read book Generative AI Journal written by Anand Vemula and published by Independently Published. This book was released on 2024-06-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI Journal: Comprehensive Guide and Practical Solutions is an in-depth exploration of generative artificial intelligence, designed to equip readers with both theoretical knowledge and practical skills. This book is structured to cater to a diverse audience, from AI enthusiasts and researchers to professionals seeking to apply generative AI in real-world scenarios. The journey begins with an introduction to generative AI, explaining what it is, its historical evolution, and the fundamental concepts and terminologies essential to understanding the field. It then delves into the importance and impact of generative AI across various industries, highlighting its transformative potential. The foundation of generative AI is laid out through a detailed examination of machine learning basics, deep learning fundamentals, and neural network overviews. Key generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are thoroughly explained, including their architectures, types, and practical applications. The book explores advanced topics such as transfer learning, reinforcement learning in generative AI, data augmentation, and zero-shot and few-shot learning, providing readers with cutting-edge techniques to enhance their generative models. Ethical considerations, including deepfakes, bias, fairness, and privacy concerns, are addressed to ensure responsible AI practices. Practical implementations are a core focus, with hands-on tutorials using popular tools and libraries like TensorFlow and PyTorch. Readers will learn how to build, train, optimize, deploy, and scale generative models. Detailed case studies, including GANs in art creation, VAEs in medical imaging, and text generation for content creation, illustrate the real-world impact and versatility of generative AI. Looking towards the future, the book discusses emerging trends, potential applications, and research frontiers in generative AI, offering insights into the evolving landscape and its implications. Generative AI Journal: Comprehensive Guide and Practical Solutions is an essential resource for anyone looking to master the art and science of generative AI, blending theoretical foundations with practical insights and applications.

Download or read book Foundations of Deep Learning written by Tapomoy Adhikari and published by Tapomoy Adhikari. This book was released on 2023-09-04 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Foundations of Deep Learning" offers an erudite exploration into the dynamic landscape of artificial intelligence (AI) and deep learning, authored by Tapomoy Adhikari, an autonomous researcher in the field of Computer Science and Engineering. This scholarly work provides a comprehensive resource suitable for individuals at various stages of expertise, ranging from neophytes to seasoned practitioners within the domain of neural networks. Commencing with an introductory exposition, the book elucidates fundamental principles integral to deep learning. Subsequently, it undertakes a rigorous examination of neural network architectures, elucidating their constituent elements, activation functions, and optimization methodologies. The discourse extends to encompass the intricate mechanisms of backpropagation, a cornerstone process in neural network training. Further chapters delve deeply into Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), elucidating their pivotal roles across diverse applications such as computer vision and natural language processing. Noteworthy concepts explored include Generative Adversarial Networks (GANs), Attention Mechanisms, and Transfer Learning, furnishing readers with a comprehensive toolkit to address real-world challenges. In light of burgeoning ethical concerns within the AI landscape, the book offers nuanced insights into ethical considerations pertinent to deep learning. Emphasis is placed on responsible AI model development and its societal implications. The discourse extends to encompass the domain of Natural Language Processing (NLP) integrated with deep learning, elucidating concepts such as word embeddings and sequence-to-sequence models, alongside the transformative potential of attention mechanisms. Deep Reinforcement Learning, a pivotal paradigm underpinning gaming AI and autonomous systems, undergoes meticulous scrutiny, equipping readers with the requisite knowledge to navigate this burgeoning field. As the narrative culminates, readers are prompted to contemplate the future trajectory of deep learning, exploring themes such as neuro-symbolic integration, the potential impact of quantum computing, and the ethical imperatives guiding AI development. "Foundations of Deep Learning" transcends mere instructional discourse, serving as a scholarly compendium elucidating the inner workings of AI architectures shaping contemporary society. Augmented with code snippets, diagrams, and illustrative case studies, this academic endeavor facilitates a practical and accessible understanding of complex concepts. Irrespective of readers' academic or professional affiliations, be it as students, researchers, or engineers, this scholarly treatise equips them with the requisite knowledge and methodologies to navigate the ever-evolving landscape of neural networks.

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 AI and Deep Learning Fundamentals written by Balaanand Muthu and published by . This book was released on 2023-11-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to the world of AI and Deep Learning! This book is a culmination of years of research, innovation, and practical insights into the fascinating realms of artificial intelligence and deep learning. In this rapidly evolving landscape, the convergence of neuroscience, mathematics, and computer science has sparked a revolution, redefining how machines perceive, learn, and interact with the world. As you embark on this educational journey, you'll delve into the foundational principles that underpin these technologies, unraveling complex concepts in a lucid and accessible manner. Whether you're an aspiring data scientist, a curious enthusiast, or a seasoned professional seeking to expand your knowledge, this book aims to equip you with a solid understanding of AI and deep learning essentials, empowering you to navigate the frontiers of innovation and contribute meaningfully to this transformative field. In this comprehensive guide, we traverse the landscape of artificial intelligence and deep learning, demystifying intricate theories and methodologies. From the fundamentals of neural networks to the practical applications in image recognition, natural language processing, and beyond, each chapter is meticulously crafted to provide a holistic view of these cutting-edge technologies. With a blend of theoretical foundations, real-world examples, and hands-on exercises, this book is designed to foster a deep comprehension of the core concepts while igniting your creativity to explore and innovate. Join us on this immersive journey, where you'll not only grasp the essence of AI and deep learning but also cultivate the skills to create intelligent systems that can revolutionize industries, redefine possibilities, and shape the future.

Download or read book Generative AI with Tensor Flow written by Anand Vemula and published by Anand Vemula. This book was released on with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Exploring Generative AI: From Fundamentals to Applications" is a comprehensive guide to understanding and applying generative artificial intelligence (AI). The book is divided into five parts, each covering essential aspects of generative AI, from foundational concepts to advanced techniques, practical applications, and future directions. In Part I, "Introduction to Generative AI," readers are introduced to the fundamental concepts of generative models, TensorFlow, and setting up a development environment. This section lays the groundwork for understanding how generative AI works and provides the necessary tools for experimentation. Part II, "Fundamentals of Generative Models," explores key techniques such as autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), and conditional GANs. Each chapter provides in-depth explanations of the architecture, training methods, and real-world applications of these models. In Part III, "Advanced Topics in Generative AI," readers dive deeper into advanced techniques such as deep convolutional GANs (DCGANs), StyleGAN, recurrent neural networks (RNNs) for generative sequences, and reinforcement learning for generative AI. These chapters explore cutting-edge research and developments in the field, providing insights into the latest advancements and future trends. Part IV, "Applications of Generative AI," showcases the diverse applications of generative AI across various domains, including image generation and manipulation, text generation, music generation, and healthcare. Readers learn how generative models are being used to solve real-world problems and drive innovation in fields such as healthcare, entertainment, and creative industries. Finally, in Part V, "Future Directions and Ethical Considerations," the book examines emerging trends in generative AI and addresses ethical considerations and challenges associated with the technology. Readers gain insights into the potential societal impacts of generative AI and the importance of responsible AI development and governance. "Exploring Generative AI: From Fundamentals to Applications" is a valuable resource for students, researchers, and practitioners interested in understanding and leveraging the power of generative AI to drive innovation and address complex challenges in today's rapidly evolving world.

Download or read book Deep Learning Fundamentals Theory and Applications written by Kaizhu Huang and published by Springer. This book was released on 2019-02-15 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Generative Deep Learning with Python written by Cuantum Technologies LLC and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Welcome to a journey where artificial intelligence meets creativity, where deep learning algorithms dream, and where you are the architect of these dreams. Introducing Generative Deep Learning with Python: Unleashing the Creative Power of AI - your comprehensive guide to the enchanting world of generative models. Have you ever been mesmerized by AI-created artwork, deep fake videos, or the uncanny ability of platforms like Spotify to match your music taste? At the heart of these technologies lie Generative Models, a cutting-edge AI application that's revolutionizing industries. This book is a comprehensive guide that explores this revolutionary domain. It promises to take you on a journey that cuts through the complexity and illuminates the principles that power generative models. It's a ticket to a world where art meets science, creativity aligns with technology, and AI dreams become a reality. This book is more than a guide; it's a thrilling adventure into this realm. Our journey starts with the fundamentals, demystifying concepts like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive models. This is a ticket for everyone, whether you're a seasoned AI practitioner or an enthusiastic beginner. Interest deepened? Get your hands on the three exciting projects that form the bedrock of our book: Face Generation with GANs, Handwritten Digit Generation with VAEs, and Text Generation with Autoregressive Models. These practical projects give you the opportunity to apply your knowledge and gain insights into the process of building and training generative models. The desire for more? Delve into advanced topics, exploring challenges, solutions, and prospects. From understanding and tackling the notorious problem of Mode Collapse to incorporating domain knowledge into your generative models, the book covers it all.

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Mastering Generative AI with PyTorch written by Anand Vemula and published by Independently Published. This book was released on 2024-05-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: **Mastering Generative AI with PyTorch: From Fundamentals to Advanced Models** Unlock the potential of generative artificial intelligence with "Mastering Generative AI with PyTorch." This comprehensive guide takes you on a journey from the foundational concepts of generative AI to the implementation of advanced models, providing a clear and practical roadmap for mastering this cutting-edge technology. The book begins with an introduction to the core principles of generative AI, explaining its significance and applications in various fields such as art, entertainment, and scientific research. You will explore different types of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models, gaining a deep understanding of their architectures and mechanisms. With a focus on hands-on learning, the book introduces you to PyTorch, one of the most popular and powerful deep learning frameworks. Step-by-step instructions guide you through the installation of PyTorch and fundamental operations, setting a strong foundation for building complex models. Each chapter is designed to build on the previous one, gradually increasing in complexity and depth. In the GANs section, you will learn about their architecture, training process, and advanced variations like Conditional GANs and CycleGANs. The book provides detailed code examples and explanations, enabling you to implement and train your own GANs for diverse applications. The VAE section delves into the mathematical foundations and training techniques of VAEs, including practical examples of implementing both standard and conditional VAEs with PyTorch. You'll gain insights into how VAEs can generate high-quality, realistic data and their use in creative and scientific tasks. Autoregressive models, including PixelCNN and PixelRNN, are thoroughly covered, with explanations of their applications in sequential data generation. The book also explores the integration of attention mechanisms and transformers to enhance model performance. By the end of this book, you will have a solid understanding of generative AI and be equipped with the skills to implement and experiment with various generative models using PyTorch. Whether you are a beginner or an experienced practitioner, "Mastering Generative AI with PyTorch" provides the knowledge and tools needed to excel in the exciting field of generative AI.