Generative Deep Learning

Generative Deep Learning

Author: David Foster

Publisher: "O'Reilly Media, Inc."

Published: 2019-06-28

Total Pages: 360

ISBN-13: 1492041890

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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


Book Synopsis Generative Deep Learning by : David Foster

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

Generative AI Models

Generative AI Models

Author: Jovan Pehcevski

Publisher:

Published: 2023-12

Total Pages: 0

ISBN-13: 9781774699201

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The generative AI is especially powerful in several areas, such as text generation (product description, article writing), image and video generation (AI-generated pictures and videos for marketing industry), and voice and sound generation (for film industry). This book edition covers different topics of generative AI models, including: image generation techniques, video generation techniques, speech / voice generation techniques, and societal and ethical issues of these models.


Book Synopsis Generative AI Models by : Jovan Pehcevski

Download or read book Generative AI Models written by Jovan Pehcevski and published by . This book was released on 2023-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The generative AI is especially powerful in several areas, such as text generation (product description, article writing), image and video generation (AI-generated pictures and videos for marketing industry), and voice and sound generation (for film industry). This book edition covers different topics of generative AI models, including: image generation techniques, video generation techniques, speech / voice generation techniques, and societal and ethical issues of these models.

The Potential of Generative AI

The Potential of Generative AI

Author: Divit Gupta

Publisher: BPB Publications

Published: 2024-01-06

Total Pages: 418

ISBN-13: 935551672X

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Unveiling the power and potential of Generative AI for a limitless future KEY FEATURES ● Holistic and accessible journey into Generative AI. ● Indispensable guide for unleashing Generative AI potential. ● Transforming technology, business, art, and innovation, covering technological advancements and business optimization. DESCRIPTION The Potential of Generative AI invites you for a captivating journey into the revolutionary technology, where machines become co-creators and the line between imagination and reality blurs. You will learn how AI helps doctors, engineers, and scientists solve real-world problems. Next, you will explore use cases where ChatGPT can boost productivity and enhance creativity. The book explores the journey from the origins of this revolutionary technology to its cutting-edge applications. Discover how generative models like GANs and VAEs work, and familiarize yourself with the impact they are making in fields like healthcare, finance, and art. Through real-world case studies and engaging examples, you will witness AI generating life-saving drugs, composing music, and even designing innovative products. This book explores the cutting-edge capabilities and potential of generative AI in the tech landscape. It will help you discover how generative AI can unlock new opportunities and enhance business operations. WHAT YOU WILL LEARN ● Learn about the different types of generative models, how they work, and their impact across various industries including healthcare, finance, and entertainment. ● Explore the creative potential of generative AI in art, music, and design. ● Develop Generative Adversarial Networks (GANs), with a focus on their architecture, training process, and real-world applications. ● Build and deploy generative models, ensuring readers to leverage this powerful technology. ● Perfect the art of generating text, images, music, and even code with AI, utilize your creative potential. WHO THIS BOOK IS FOR This book is for artists, programmers, musicians, designers, writers, researchers, entrepreneurs, scientists, Machine Learning practitioners and dreamers of all sorts. Generative AI awaits and is ready to transform your craft and empower your vision. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative AI in Industries 3. Fundamentals of Generative Models 4. Applications Across Industries 5. Creative Expression with Generative AI 6. Generative AI in Business and Innovation 7. Deep Dive into GANs 8. Building and Deploying Generative Models


Book Synopsis The Potential of Generative AI by : Divit Gupta

Download or read book The Potential of Generative AI written by Divit Gupta and published by BPB Publications. This book was released on 2024-01-06 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unveiling the power and potential of Generative AI for a limitless future KEY FEATURES ● Holistic and accessible journey into Generative AI. ● Indispensable guide for unleashing Generative AI potential. ● Transforming technology, business, art, and innovation, covering technological advancements and business optimization. DESCRIPTION The Potential of Generative AI invites you for a captivating journey into the revolutionary technology, where machines become co-creators and the line between imagination and reality blurs. You will learn how AI helps doctors, engineers, and scientists solve real-world problems. Next, you will explore use cases where ChatGPT can boost productivity and enhance creativity. The book explores the journey from the origins of this revolutionary technology to its cutting-edge applications. Discover how generative models like GANs and VAEs work, and familiarize yourself with the impact they are making in fields like healthcare, finance, and art. Through real-world case studies and engaging examples, you will witness AI generating life-saving drugs, composing music, and even designing innovative products. This book explores the cutting-edge capabilities and potential of generative AI in the tech landscape. It will help you discover how generative AI can unlock new opportunities and enhance business operations. WHAT YOU WILL LEARN ● Learn about the different types of generative models, how they work, and their impact across various industries including healthcare, finance, and entertainment. ● Explore the creative potential of generative AI in art, music, and design. ● Develop Generative Adversarial Networks (GANs), with a focus on their architecture, training process, and real-world applications. ● Build and deploy generative models, ensuring readers to leverage this powerful technology. ● Perfect the art of generating text, images, music, and even code with AI, utilize your creative potential. WHO THIS BOOK IS FOR This book is for artists, programmers, musicians, designers, writers, researchers, entrepreneurs, scientists, Machine Learning practitioners and dreamers of all sorts. Generative AI awaits and is ready to transform your craft and empower your vision. TABLE OF CONTENTS 1. Introduction to Generative AI 2. Generative AI in Industries 3. Fundamentals of Generative Models 4. Applications Across Industries 5. Creative Expression with Generative AI 6. Generative AI in Business and Innovation 7. Deep Dive into GANs 8. Building and Deploying Generative Models

Generative AI with Python and TensorFlow

Generative AI with Python and TensorFlow

Author: Anand Vemula

Publisher: Independently Published

Published: 2024-07-03

Total Pages: 0

ISBN-13:

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Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. Introduction Overview of Generative AI: Understand the basic concepts, history, and significance of generative AI. Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation. Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow. Fundamentals of Machine Learning Supervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning. Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI. Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative Models Understanding Generative Models: Explore the theoretical foundations of generative models. Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases. Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications. Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced Topics Transfer Learning for Generative Models: Explore how transfer learning can be applied to generative models. Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously. Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical Applications Image Generation and Style Transfer: Create stunning images and apply style transfer techniques. Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI. Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On Projects Project 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art. Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction. Project 4: Music Generation with RNNs: Create a music generation model using RNNs.


Book Synopsis Generative AI with Python and TensorFlow by : Anand Vemula

Download or read book Generative AI with Python and TensorFlow written by Anand Vemula and published by Independently Published. This book was released on 2024-07-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI with Python and TensorFlow: A Complete Guide to Mastering AI Models is a comprehensive resource for anyone looking to delve into the world of generative artificial intelligence. Introduction Overview of Generative AI: Understand the basic concepts, history, and significance of generative AI. Importance of Generative AI: Learn about the transformative potential of generative AI in various industries. Applications and Use Cases: Explore real-world applications of generative AI in fields such as art, music, text generation, and data augmentation. Overview of Python and TensorFlow: Get an introduction to the essential tools and libraries used for building generative AI models. Getting Started: Set up your development environment, install necessary libraries, and take your first steps with TensorFlow. Fundamentals of Machine Learning Supervised vs. Unsupervised Learning: Understand the differences and use cases of these two primary types of machine learning. Neural Networks Basics: Learn the fundamental concepts of neural networks and their role in AI. Introduction to Deep Learning: Dive deeper into the advanced techniques of deep learning and its applications in generative AI. Key Concepts in Generative AI: Familiarize yourself with the essential concepts and terminologies in generative AI. Generative Models Understanding Generative Models: Explore the theoretical foundations of generative models. Types of Generative Models: Learn about various types of generative models, including VAEs, GANs, autoregressive models, and flow-based models. Variational Autoencoders (VAEs): Delve into the theory behind VAEs, build and train VAEs with TensorFlow, and explore their use cases. Generative Adversarial Networks (GANs): Get introduced to GANs, understand their architecture, implement GANs with TensorFlow, and learn advanced GAN techniques. Autoregressive Models: Understand autoregressive models, implement them with TensorFlow, and explore their applications. Flow-based Models: Learn about flow-based models, build them with TensorFlow, and explore their practical applications. Advanced Topics Transfer Learning for Generative Models: Explore how transfer learning can be applied to generative models. Conditional Generative Models: Understand and implement models that generate outputs conditioned on specific inputs. Multimodal Generative Models: Learn about models that can generate multiple types of data simultaneously. Reinforcement Learning in Generative AI: Explore the intersection of reinforcement learning and generative AI. Practical Applications Image Generation and Style Transfer: Create stunning images and apply style transfer techniques. Text Generation and Natural Language Processing: Generate coherent and contextually relevant text using advanced NLP techniques. Music and Sound Generation: Compose music and generate new sounds using generative AI. Data Augmentation for Machine Learning: Improve your machine learning models by augmenting your datasets with generative models. Hands-On Projects Project 1: Creating Art with GANs: Step-by-step guide to building a GAN to generate art. Project 2: Text Generation with LSTM: Implement an LSTM model for generating text. Project 3: Building a VAE for Image Reconstruction: Learn how to build and train a VAE for image reconstruction. Project 4: Music Generation with RNNs: Create a music generation model using RNNs.

Generative AI with Python and TensorFlow 2

Generative AI with Python and TensorFlow 2

Author: Joseph Babcock

Publisher: Packt Publishing Ltd

Published: 2021-04-30

Total Pages: 489

ISBN-13: 1800208502

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Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.


Book Synopsis Generative AI with Python and TensorFlow 2 by : Joseph Babcock

Download or read book Generative AI with Python and TensorFlow 2 written by Joseph Babcock and published by Packt Publishing Ltd. This book was released on 2021-04-30 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

Generative Deep Learning

Generative Deep Learning

Author: David Foster

Publisher: "O'Reilly Media, Inc."

Published: 2022-06-28

Total Pages: 456

ISBN-13: 109813415X

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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.


Book Synopsis Generative Deep Learning by : David Foster

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.

Generative AI.

Generative AI.

Author: World Intellectual Property Organization

Publisher: WIPO

Published: 2024-07-15

Total Pages: 12

ISBN-13:

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Generative artificial intelligence (AI) has the potential to disrupt every industry and many parts of our lives – promising both competitive advantage and creative destruction. But how creative and imaginative is generative AI, what are the potential implications for human creators and how do we ensure that the IP system continues to foster innovation and creativity in the age of AI?


Book Synopsis Generative AI. by : World Intellectual Property Organization

Download or read book Generative AI. written by World Intellectual Property Organization and published by WIPO. This book was released on 2024-07-15 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative artificial intelligence (AI) has the potential to disrupt every industry and many parts of our lives – promising both competitive advantage and creative destruction. But how creative and imaginative is generative AI, what are the potential implications for human creators and how do we ensure that the IP system continues to foster innovation and creativity in the age of AI?

Generative AI and Large Language Models

Generative AI and Large Language Models

Author: Aditya Pratap Bhuyan

Publisher: Aditya Pratap Bhuyan

Published: 2024-07-24

Total Pages: 109

ISBN-13:

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Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.


Book Synopsis Generative AI and Large Language Models by : Aditya Pratap Bhuyan

Download or read book Generative AI and Large Language Models written by Aditya Pratap Bhuyan and published by Aditya Pratap Bhuyan. This book was released on 2024-07-24 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.

Learn Python Generative AI

Learn Python Generative AI

Author: Zonunfeli Ralte

Publisher: BPB Publications

Published: 2024-02-01

Total Pages: 421

ISBN-13: 9355518978

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Learn to unleash the power of AI creativity KEY FEATURES ● Understand the core concepts related to generative AI. ● Different types of generative models and their applications. ● Learn how to design generative AI neural networks using Python and TensorFlow. DESCRIPTION This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making. The book concludes by synthesizing key learnings, offering insights into the future of generative AI, and making it a comprehensive guide for diverse industries. Readers will find themselves equipped with a profound understanding of generative AI, its current applications, and its boundless potential for future innovations. WHAT YOU WILL LEARN ● Acquire practical skills in designing and implementing various generative AI models. ● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval. ● Navigate challenges in healthcare, retail, and finance using sector specific insights. ● Generate images and text with VAEs, GANs, LLMs, and vector databases. ● Focus on both traditional and cutting edge techniques in generative AI. WHO THIS BOOK IS FOR This book is for current and aspiring emerging AI deep learning professionals, architects, students, and anyone who is starting and learning a rewarding career in generative AI. TABLE OF CONTENTS 1. Introducing Generative AI 2. Designing Generative Adversarial Networks 3. Training and Developing Generative Adversarial Networks 4. Architecting Auto Encoder for Generative AI 5. Building and Training Generative Autoencoders 6. Designing Generative Variation Auto Encoder 7. Building Variational Autoencoders for Generative AI 8. Fundamental of Designing New Age Generative Vision Transformer 9. Implementing Generative Vision Transformer 10. Architectural Refactoring for Generative Modeling 11. Major Technical Roadblocks in Generative AI and Way Forward 12. Overview and Application of Generative AI Models 13. Key Learnings


Book Synopsis Learn Python Generative AI by : Zonunfeli Ralte

Download or read book Learn Python Generative AI written by Zonunfeli Ralte and published by BPB Publications. This book was released on 2024-02-01 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to unleash the power of AI creativity KEY FEATURES ● Understand the core concepts related to generative AI. ● Different types of generative models and their applications. ● Learn how to design generative AI neural networks using Python and TensorFlow. DESCRIPTION This book researches the intricate world of generative Artificial Intelligence, offering readers an extensive understanding of various components and applications in this field. The book begins with an in-depth analysis of generative models, providing a solid foundation and exploring their combination nuances. It then focuses on enhancing TransVAE, a variational autoencoder, and introduces the Swin Transformer in generative AI. The inclusion of cutting edge applications like building an image search using Pinecone and a vector database further enriches its content. The narrative shifts to practical applications, showcasing GenAI's impact in healthcare, retail, and finance, with real-world examples and innovative solutions. In the healthcare sector, it emphasizes AI's transformative role in diagnostics and patient care. In retail and finance, it illustrates how AI revolutionizes customer engagement and decision making. The book concludes by synthesizing key learnings, offering insights into the future of generative AI, and making it a comprehensive guide for diverse industries. Readers will find themselves equipped with a profound understanding of generative AI, its current applications, and its boundless potential for future innovations. WHAT YOU WILL LEARN ● Acquire practical skills in designing and implementing various generative AI models. ● Gain expertise in vector databases and image embeddings, crucial for image search and data retrieval. ● Navigate challenges in healthcare, retail, and finance using sector specific insights. ● Generate images and text with VAEs, GANs, LLMs, and vector databases. ● Focus on both traditional and cutting edge techniques in generative AI. WHO THIS BOOK IS FOR This book is for current and aspiring emerging AI deep learning professionals, architects, students, and anyone who is starting and learning a rewarding career in generative AI. TABLE OF CONTENTS 1. Introducing Generative AI 2. Designing Generative Adversarial Networks 3. Training and Developing Generative Adversarial Networks 4. Architecting Auto Encoder for Generative AI 5. Building and Training Generative Autoencoders 6. Designing Generative Variation Auto Encoder 7. Building Variational Autoencoders for Generative AI 8. Fundamental of Designing New Age Generative Vision Transformer 9. Implementing Generative Vision Transformer 10. Architectural Refactoring for Generative Modeling 11. Major Technical Roadblocks in Generative AI and Way Forward 12. Overview and Application of Generative AI Models 13. Key Learnings

Generative AI with Large Language Models: A Comprehensive Guide

Generative AI with Large Language Models: A Comprehensive Guide

Author: Anand Vemula

Publisher: Anand Vemula

Published:

Total Pages: 43

ISBN-13:

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This book delves into the fascinating world of Generative AI, exploring the two key technologies driving its advancements: Large Language Models (LLMs) and Foundation Models (FMs). Part 1: Foundations LLMs Demystified: We begin by understanding LLMs, powerful AI models trained on massive amounts of text data. These models can generate human-quality text, translate languages, write different creative formats, and even answer your questions in an informative way. The Rise of FMs: However, LLMs are just a piece of the puzzle. We explore Foundation Models, a broader category encompassing models trained on various data types like images, audio, and even scientific data. These models represent a significant leap forward in AI, offering a more versatile approach to information processing. Part 2: LLMs and Generative AI Applications Training LLMs: We delve into the intricate process of training LLMs, from data acquisition and pre-processing to different training techniques like supervised and unsupervised learning. The chapter also explores challenges like computational resources and data bias, along with best practices for responsible LLM training. Fine-Tuning for Specific Tasks: LLMs can be further specialized for targeted tasks through fine-tuning. We explore how fine-tuning allows LLMs to excel in areas like creative writing, code generation, drug discovery, and even music composition. Part 3: Advanced Topics LLM Architectures: We take a deep dive into the technical aspects of LLMs, exploring the workings of Transformer networks, the backbone of modern LLMs. We also examine the role of attention mechanisms in LLM processing and learn about different prominent LLM architectures like GPT-3 and Jurassic-1 Jumbo. Scaling Generative AI: Scaling up LLMs presents significant computational challenges. The chapter explores techniques like model parallelism and distributed training to address these hurdles, along with hardware considerations like GPUs and TPUs that facilitate efficient LLM training. Most importantly, we discuss the crucial role of safety and ethics in generative AI development. Mitigating bias, addressing potential risks like deepfakes, and ensuring transparency are all essential for responsible AI development. Part 4: The Future Evolving Generative AI Landscape: We explore emerging trends in LLM research, like the development of even larger and more capable models, along with advancements in explainable AI and the rise of multimodal LLMs that can handle different data types. We also discuss the potential applications of generative AI in unforeseen areas like personalized education and healthcare. Societal Impact and the Future of Work: The book concludes by examining the societal and economic implications of generative AI. We explore the potential transformation of industries, the need for workforce reskilling, and the importance of human-AI collaboration. Additionally, the book emphasizes the need for robust regulations to address concerns like bias, data privacy, and transparency in generative AI development. This book equips you with a comprehensive understanding of generative AI, its core technologies, its applications, and the considerations for its responsible development and deployment.


Book Synopsis Generative AI with Large Language Models: A Comprehensive Guide by : Anand Vemula

Download or read book Generative AI with Large Language Models: A Comprehensive Guide written by Anand Vemula and published by Anand Vemula. This book was released on with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book delves into the fascinating world of Generative AI, exploring the two key technologies driving its advancements: Large Language Models (LLMs) and Foundation Models (FMs). Part 1: Foundations LLMs Demystified: We begin by understanding LLMs, powerful AI models trained on massive amounts of text data. These models can generate human-quality text, translate languages, write different creative formats, and even answer your questions in an informative way. The Rise of FMs: However, LLMs are just a piece of the puzzle. We explore Foundation Models, a broader category encompassing models trained on various data types like images, audio, and even scientific data. These models represent a significant leap forward in AI, offering a more versatile approach to information processing. Part 2: LLMs and Generative AI Applications Training LLMs: We delve into the intricate process of training LLMs, from data acquisition and pre-processing to different training techniques like supervised and unsupervised learning. The chapter also explores challenges like computational resources and data bias, along with best practices for responsible LLM training. Fine-Tuning for Specific Tasks: LLMs can be further specialized for targeted tasks through fine-tuning. We explore how fine-tuning allows LLMs to excel in areas like creative writing, code generation, drug discovery, and even music composition. Part 3: Advanced Topics LLM Architectures: We take a deep dive into the technical aspects of LLMs, exploring the workings of Transformer networks, the backbone of modern LLMs. We also examine the role of attention mechanisms in LLM processing and learn about different prominent LLM architectures like GPT-3 and Jurassic-1 Jumbo. Scaling Generative AI: Scaling up LLMs presents significant computational challenges. The chapter explores techniques like model parallelism and distributed training to address these hurdles, along with hardware considerations like GPUs and TPUs that facilitate efficient LLM training. Most importantly, we discuss the crucial role of safety and ethics in generative AI development. Mitigating bias, addressing potential risks like deepfakes, and ensuring transparency are all essential for responsible AI development. Part 4: The Future Evolving Generative AI Landscape: We explore emerging trends in LLM research, like the development of even larger and more capable models, along with advancements in explainable AI and the rise of multimodal LLMs that can handle different data types. We also discuss the potential applications of generative AI in unforeseen areas like personalized education and healthcare. Societal Impact and the Future of Work: The book concludes by examining the societal and economic implications of generative AI. We explore the potential transformation of industries, the need for workforce reskilling, and the importance of human-AI collaboration. Additionally, the book emphasizes the need for robust regulations to address concerns like bias, data privacy, and transparency in generative AI development. This book equips you with a comprehensive understanding of generative AI, its core technologies, its applications, and the considerations for its responsible development and deployment.