Hands On Generative Ai With Transformers And Diffusion Models

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Hands On Generative Ai With Transformers And Diffusion Models
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Author : Omar Sanseviero
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-11-22
Hands On Generative Ai With Transformers And Diffusion Models written by Omar Sanseviero and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-22 with Computers categories.
Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model and fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train models that can reflect your own unique style
Hands On Generative Ai With Transformers And Diffusion Models
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Author : Omar Sanseviero
language : en
Publisher:
Release Date : 2025-01-28
Hands On Generative Ai With Transformers And Diffusion Models written by Omar Sanseviero and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Computers categories.
Learn how to use generative media techniques with AI to create novel images or music in this practical, hands-on guide. Data scientists and software engineers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to your needs, and how to combine existing building blocks to create new models and creative applications in different domains. This book introduces theoretical concepts in an intuitive way, with extensive code samples and illustrations that you can run on services such as Google Colaboratory, Kaggle, or Hugging Face Spaces with minimal setup. You'll learn how to use open source libraries such as Transformers and Diffusers, conduct code exploration, and study several existing projects to help guide your work. Learn the fundamentals of classic and modern generative AI techniques Build and customize models that can generate text, images, and sound Explore trade-offs between training from scratch and using large, pretrained models Create models that can modify images by transferring the style of other images Tweak and bend transformers and diffusion models for creative purposes Train a model that can write text based on your style Deploy models as interactive demos or services
Hands On Generative Ai With Transformers And Diffusion Models
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Author : Omar Sanseviero
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2024-11-22
Hands On Generative Ai With Transformers And Diffusion Models written by Omar Sanseviero and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-22 with Computers categories.
Learn to use generative AI techniques to create novel text, images, audio, and even music with this practical, hands-on book. Readers will understand how state-of-the-art generative models work, how to fine-tune and adapt them to their needs, and how to combine existing building blocks to create new models and creative applications in different domains. This go-to book introduces theoretical concepts followed by guided practical applications, with extensive code samples and easy-to-understand illustrations. You'll learn how to use open source libraries to utilize transformers and diffusion models, conduct code exploration, and study several existing projects to help guide your work. Build and customize models that can generate text and images Explore trade-offs between using a pretrained model and fine-tuning your own model Create and utilize models that can generate, edit, and modify images in any style Customize transformers and diffusion models for multiple creative purposes Train models that can reflect your own unique style
Engineering Applications Of Neural Networks
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Author : Lazaros Iliadis
language : en
Publisher: Springer Nature
Release Date : 2025-07-23
Engineering Applications Of Neural Networks written by Lazaros Iliadis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-23 with Computers categories.
The two-volume set CCIS 2581 and 2582 constitutes the refereed proceedings of the 26th International Conference on Engineering Applications of Neural Networks, EANN 2025, held in Limassol, Cyprus during June 26–29, 2025. The 41 full papers included in these proceedings were carefully reviewed and selected from 101 submissions. These papers demonstrate the vitality of Artificial Intelligence algorithms and approaches, as well as AI applications.
Hands On Machine Learning With C
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Author : Kirill Kolodiazhnyi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-24
Hands On Machine Learning With C written by Kirill Kolodiazhnyi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-24 with Computers categories.
Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets Key Features Familiarize yourself with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a seasoned software engineer with several years of industry experience, this book will teach you the basics of machine learning (ML) and show you how to use C++ libraries, along with helping you create supervised and unsupervised ML models. You’ll gain hands-on experience in tuning and optimizing a model for various use cases, enabling you to efficiently select models and measure performance. The chapters cover techniques such as product recommendations, ensemble learning, anomaly detection, sentiment analysis, and object recognition using modern C++ libraries. You’ll also learn how to overcome production and deployment challenges on mobile platforms, and see how the ONNX model format can help you accomplish these tasks. This new edition has been updated with key topics such as sentiment analysis implementation using transfer learning and transformer-based models, as well as tracking and visualizing ML experiments with MLflow. An additional section shows you how to use Optuna for hyperparameter selection. The section on model deployment into mobile platform now includes a detailed explanation of real-time object detection for Android with C++. By the end of this C++ book, you’ll have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.What you will learn Employ key machine learning algorithms using various C++ libraries Load and pre-process different data types to suitable C++ data structures Find out how to identify the best parameters for a machine learning model Use anomaly detection for filtering user data Apply collaborative filtering to manage dynamic user preferences Utilize C++ libraries and APIs to manage model structures and parameters Implement C++ code for object detection using a modern neural network Who this book is for This book is for beginners looking to explore machine learning algorithms and techniques using C++. This book is also valuable for data analysts, scientists, and developers who want to implement machine learning models in production. Working knowledge of C++ is needed to make the most of this book.
Building Generative Ai Powered Apps
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Author : Aarushi Kansal
language : en
Publisher: Springer Nature
Release Date : 2024-03-23
Building Generative Ai Powered Apps written by Aarushi Kansal and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-23 with Computers categories.
Generative AI has gone beyond the responsibility of researchers and data scientists and is being used by production engineers. However, there is a lot of confusion where to get started when building an end-to-end app with generative AI. This book consolidates core models, frameworks, and tools into a single source of knowledge. By providing hands-on examples, the book takes you through the generative AI ecosystem to build applications for production. The book starts with a brief and accessible introduction to transformer models before delving into some of the most popular large language models and diffusions models (image generation). These models are the foundations of both AI and your potential new apps. You will then go through various tools available to work with these models, starting with Langchain, a framework to develop foundational models, which is the next building block you should grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included. By the end of this book, you should be able to build end-to-end apps that are powered by generative AI. You also should be able to apply the tools and techniques taught in this book to your use cases and business. What You Will Learn What is Generative AI? What is ChatGPT and GPT4? What are language models and diffusions models? How do we deploy LangChain and HuggingFace? Who This Book Is For Software engineers with a few years of experience building applications in any language or infrastructure
Ai
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Author : 오마르 산세비에로Omar Sanseviero
language : ko
Publisher: 한빛미디어
Release Date : 2025-06-30
Ai written by 오마르 산세비에로Omar Sanseviero and has been published by 한빛미디어 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Computers categories.
생성형 AI, 이론을 넘어 실전에서 완성하다 허깅페이스 코어 개발자와 함께 한 권으로 마스터하는 GenAI 실무 구현 허깅페이스 코어 개발한 저자가 직접 최신 기술을 직접 구현하며 이론과 실무의 간극과 기술 갈증을 해소해 드립니다. 프로프트 엔지니어링을 넘어 트랜스포머와 확산 모델의 내부 구조를 탐구하면서 생성형 AI의 핵심 원리를 이해하고, 최신 기술을 직접 구현해보며 학습할 수 있도록 구성되어 있습니다. 트랜스포머와 확산 모델을 중심으로 생성형 AI의 주요 구조와 동작 방식을 다루고, 이미지·텍스트·오디오를 생성하는 멀티모달 모델의 작동 방식과 활용법을 깊이 있게 설명합니다. 오토인코더, CLIP, U-Net 등 핵심 개념을 정리하고, 텍스트 생성, 조건부 이미지 생성, 오디오 생성까지 다양한 프로젝트 중심 실습을 통해 개념과 구현을 함께 익힐 수 있도록 구성했습니다. 특히 허깅 페이스와 구글 코랩 기반의 환경을 활용해 복잡한 설정 없이 직접 실습할 수 있으며, 스테이블 디퓨전, 드림부스, LoRA 같은 최신 기술도 단계적으로 구현해볼 수 있습니다. 또한 텍스트 분류, 생성, 지시어 기반 파인튜닝부터 검색 증강 생성(RAG) 구현까지 실무에 필요한 전이 학습 기법을 실제 코드와 함께 소개하며, 인페인팅, 이미지 편집, 컨트롤넷 등 창의적인 활용 예제는 물론, 멀티모달, 3D 비전, 비디오 생성 등 최신 생성형 AI 기술의 발전 흐름도 함께 짚어봅니다. 생성형 AI를 실무에 활용하려는 개발자에게 이 책은 기술 원리부터 구현, 응용까지 한 권으로, 체계적으로 설명하는 좋은 안내서가 되어 줄 것입니다.
Generative Ai Foundations In Python
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Author : Carlos Rodriguez
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-26
Generative Ai Foundations In Python written by Carlos Rodriguez and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-26 with Computers categories.
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.
Generative Ai From Beginner To Paid Professional Part 3
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Author : Bolakale Aremu
language : en
Publisher: AB Publisher LLC
Release Date : 2024-11-19
Generative Ai From Beginner To Paid Professional Part 3 written by Bolakale Aremu and has been published by AB Publisher LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Computers categories.
Dive deep into the world of Hugging Face and unlock the tools you need to create, fine-tune, and deploy state-of-the-art AI models. Part 3 of the Generative AI from Beginner to Paid Professional series is your complete guide to mastering Hugging Face’s powerful ecosystem through practical projects and real-world applications. This book takes you beyond the basics, providing hands-on exercises and expert insights to help you leverage Hugging Face for NLP, vision tasks, and beyond. You'll not only learn to work with pretrained models but also gain the skills to customize and deploy AI solutions that solve real-world problems. What’s inside: > Practical Hands-On Learning: Master Hugging Face tools by building projects like text summarization, chatbots, and image classification. > Advanced Techniques: Learn fine-tuning, model optimization, and efficient inference for high-performance applications. > Real-World Deployments: Understand how to host models on Hugging Face Spaces and integrate them into pipelines with tools like LangChain. > Production-Ready Projects: Get step-by-step guidance on creating deployable AI solutions, from concept to implementation. By the end of this book, you’ll have the confidence and skills to design and deliver professional-grade AI solutions, whether for personal projects, freelance opportunities, or enterprise applications. Who this book is for: This guide is perfect for data scientists, AI enthusiasts, and developers eager to take their skills to the next level and monetize their knowledge. Whether you're a student or a professional, Part 3 will prepare you to build innovative solutions and thrive in the booming AI industry. Take the leap into AI mastery and start creating the future.
Hands On Machine Learning With Scikit Learn Keras And Tensorflow
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Author : Aurélien Géron
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-10-04
Hands On Machine Learning With Scikit Learn Keras And Tensorflow written by Aurélien Géron and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-04 with Computers categories.
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started. Use Scikit-learn to track an example ML project end to end Explore several models, including support vector machines, decision trees, random forests, and ensemble methods Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning