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Author: Andrew Zhu (Shudong Zhu)
Publisher: Packt Publishing Ltd
ISBN: 1835084311
Category : Computers
Languages : en
Pages : 352
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Book Description
Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design in this hands-on guide, with key images printed in color Key Features Master the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python code Get instant access to emerging extensions and open-source models Leverage the power of community-shared models and LoRA to produce high-quality images that captivate audiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionStable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.What you will learn Explore core concepts and applications of Stable Diffusion and set up your environment for success Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality Explore developments in Stable Diffusion such as video generation using AnimateDiff Write effective prompts and leverage LLMs to automate the process Discover how to train a Stable Diffusion LoRA from scratch Who this book is for If you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.
Author: Andrew Zhu (Shudong Zhu)
Publisher: Packt Publishing Ltd
ISBN: 1835084311
Category : Computers
Languages : en
Pages : 352
Get Book Here
Book Description
Master AI image generation by leveraging GenAI tools and techniques such as diffusers, LoRA, textual inversion, ControlNet, and prompt design in this hands-on guide, with key images printed in color Key Features Master the art of generating stunning AI artwork with the help of expert guidance and ready-to-run Python code Get instant access to emerging extensions and open-source models Leverage the power of community-shared models and LoRA to produce high-quality images that captivate audiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionStable Diffusion is a game-changing AI tool that enables you to create stunning images with code. The author, a seasoned Microsoft applied data scientist and contributor to the Hugging Face Diffusers library, leverages his 15+ years of experience to help you master Stable Diffusion by understanding the underlying concepts and techniques. You’ll be introduced to Stable Diffusion, grasp the theory behind diffusion models, set up your environment, and generate your first image using diffusers. You'll optimize performance, leverage custom models, and integrate community-shared resources like LoRAs, textual inversion, and ControlNet to enhance your creations. Covering techniques such as face restoration, image upscaling, and image restoration, you’ll focus on unlocking prompt limitations, scheduled prompt parsing, and weighted prompts to create a fully customized and industry-level Stable Diffusion app. This book also looks into real-world applications in medical imaging, remote sensing, and photo enhancement. Finally, you'll gain insights into extracting generation data, ensuring data persistence, and leveraging AI models like BLIP for image description extraction. By the end of this book, you'll be able to use Python to generate and edit images and leverage solutions to build Stable Diffusion apps for your business and users.What you will learn Explore core concepts and applications of Stable Diffusion and set up your environment for success Refine performance, manage VRAM usage, and leverage community-driven resources like LoRAs and textual inversion Harness the power of ControlNet, IP-Adapter, and other methodologies to generate images with unprecedented control and quality Explore developments in Stable Diffusion such as video generation using AnimateDiff Write effective prompts and leverage LLMs to automate the process Discover how to train a Stable Diffusion LoRA from scratch Who this book is for If you're looking to gain control over AI image generation, particularly through the diffusion model, this book is for you. Moreover, data scientists, ML engineers, researchers, and Python application developers seeking to create AI image generation applications based on the Stable Diffusion framework can benefit from the insights provided in the book.
Author: Ivan Vasilev
Publisher: Packt Publishing Ltd
ISBN: 1837633452
Category : Computers
Languages : en
Pages : 362
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Book Description
Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python Key Features Understand the theory, mathematical foundations and structure of deep neural networks Become familiar with transformers, large language models, and convolutional networks Learn how to apply them to various computer vision and natural language processing problems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.What you will learn Establish theoretical foundations of deep neural networks Understand convolutional networks and apply them in computer vision applications Become well versed with natural language processing and recurrent networks Explore the attention mechanism and transformers Apply transformers and large language models for natural language and computer vision Implement coding examples with PyTorch, Keras, and Hugging Face Transformers Use MLOps to develop and deploy neural network models Who this book is for This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.
Author: Micheal Lanham
Publisher: BPB Publications
ISBN: 9355516436
Category : Computers
Languages : en
Pages : 408
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Book Description
Leverage the power of AI in coding, graphics, design, and intelligence to join the next wave in game development KEY FEATURES ● Teaches the core concepts of game development for 2D, 3D, and AI games. ● Uses AI to assist and guide the reader across several facets of game development. ● Learn to create AI-controlled enemies for your games. DESCRIPTION This book is a comprehensive guide to creating interactive and engaging games, leveraging the capabilities of ChatGPT and other advanced AI technologies. The book starts with prompt engineering and system prompting, building a strong AI foundation for game development. It covers various game genres, from text adventures to 3D shooters, showing AI integration. Each chapter is designed to build on the previous one, ensuring a cohesive learning experience that gradually increases in complexity and depth. Readers will learn game development basics and creative techniques for immersive game worlds. They will use PyZork for text games and Streamlit for enhanced visuals. The book covers AI-generated assets, behavior-driven AI, and advanced topics like isometric world-building and voice-responsive games. Practical projects help readers create their unique games, while GPT agents and AI technologies showcase the future of gaming. By the end of this journey, readers will have a deep understanding of how to create innovative and engaging games using AI, positioning them at the forefront of modern game development. WHAT YOU WILL LEARN ● Master prompt engineering for building games, game assets, and AI-driven games. ● Develop engaging text-based adventures with AI-driven storytelling elements. ● Create 2D games from platformers, isometric worlds, and physics. ● Design AI opponents with behavior-driven logic and adaptive difficulty. ● Introduction to 3D first-person shooters using GPT agents. ● Implement voice recognition and text-to-speech in interactive games. WHO THIS BOOK IS FOR This book is for aspiring or experienced game developers and indie game studios interested in using generative AI to create games faster and explore new possibilities. TABLE OF CONTENTS 1. ChatGPT and the Magic of Prompt Engineering 2. Text Adventure: Entering the Enchanted Realm 3. The AI Chronicles: Text Game Evolution 4. 2D Platformer: Leap into Pixelated Fun! 5. Bot Brawls: AI Opponents Enter the Arena 6. Revving up: Cars, Ramps, and Pymunk 7. Building Isometric Worlds 8. Leveling up with GPT Agents and AutoGen 9. Building a 3D First-Person Shooter 10. Games That Respond to Your Voice 11. The Future Beckons: Developing GPT Games
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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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: Sinan Ozdemir
Publisher: Addison-Wesley Professional
ISBN: 013534655X
Category : Computers
Languages : en
Pages : 584
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Book Description
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems. Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family). Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more Use APIs and Python to fine-tune and customize LLMs for your requirements Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5 Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field." --Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Richi Nayak
Publisher: Springer Nature
ISBN: 9819711118
Category :
Languages : en
Pages : 409
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Book Description
Author: Denis Rothman
Publisher: Packt Publishing Ltd
ISBN: 1805123742
Category : Computers
Languages : en
Pages : 731
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Book Description
The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AI Key Features Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Purchase of the print or Kindle book includes a free eBook in PDF format Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.What you will learn Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM 2 models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V Who this book is for This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field. Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.
Author: Pierpaolo Ruttico
Publisher: Springer Nature
ISBN: 3031479130
Category : Technology & Engineering
Languages : en
Pages : 298
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Book Description
This book provides a clear picture of how computational processes are gradually permeating and innovating the Architecture, Engineering, and Construction sector, contributing to sustainability and aesthetic evolution. It achieves that by gathering a collection of accounts shared by pioneering professionals involved in this innovation, drawing from recent academic studies, ongoing experimental processes conducted in cutting-edge architectural and engineering offices, as well as innovative industrial applications. The covered subjects span a wide range, including artificial intelligence and robotic manufacturing, the metaverse and 3D printing, strategies to counter CO2 consumption through plug-ins, as well as emerging materials and construction techniques. The chapters feature authors who are pioneers and embrace roles like software developers, architects, process engineers, academics, and forward-thinking entrepreneurs. They represent authoritative references within a broader interconnected cultural and technological system; an eclectic system that finds in computational processes the key to addressing the new challenges of contemporary architecture.
Author: Alexander Chesalov
Publisher: Litres
ISBN: 5046292963
Category : Computers
Languages : en
Pages : 387
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Book Description
This book is written for middle and high school students.With the help of it and the Easy Diffusion 3.0 artificial intelligence system, you will learn how to create unique and inimitable images that you can use in your studies or for entertainment.The book contains educational information about existing types of artificial intelligence and a wonderful album of more than a hundred illustrations.
Author: Pablo Lorenzo-Eiroa
Publisher: Taylor & Francis
ISBN: 1000882667
Category : Architecture
Languages : en
Pages : 491
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Book Description
This book proposes a new critical relationship between computation and architecture, developing a history and theory of representation in architecture to understand and unleash potential means to open up creativity in the field. Historically, architecture has led to spatial representation. Today, computation has established new representational paradigms that can be compared to spatial representations, such as the revolution of perspective in the Renaissance. Architects now use software, robotics, and fabrication tools with very little understanding and participation in how these tools influence, revolutionize, and determine both architecture and its construction today. Why does the discipline of architecture not have a higher degree of authorship in the conception and development of computational technologies that define spatial representation? This book critically explores the relationship between history, theory, and cultural criticism. Lorenzo-Eiroa positions new understandings through parallel historical sections and theories of many revolutionary representational architecture canons displaced by conventional spatial projection. He identifies the architects, artists, mathematicians, and philosophers that were able to revolutionize their disciplines through the development of new technologies, new systems of representation, and new lenses to understand reality. This book frames the discussion by addressing new means to understand and expand architecture authorship in relation to the survey, information, representation, higher dimensional space, Big Data, and Artificial Intelligence – in the pursuit of activating an architecture of information. This will be important reading for upper-level students and researchers of architecture and architectural theory, especially those with a keen interest in computational design and robotic fabrication.
