Hands-On Generative AI with Transformers and Diffusion Models PDF Download
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Author: Omar Sanseviero
Publisher:
ISBN: 9781098149246
Category : Computers
Languages : en
Pages : 0
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Book Description
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
Author: Omar Sanseviero
Publisher:
ISBN: 9781098149246
Category : Computers
Languages : en
Pages : 0
Get Book Here
Book Description
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
Author: Omar Sanseviero
Publisher: "O'Reilly Media, Inc."
ISBN: 1098149203
Category : Computers
Languages : en
Pages : 425
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Book Description
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
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: Kirill Kolodiazhnyi
Publisher: Packt Publishing Ltd
ISBN: 1805126148
Category : Computers
Languages : en
Pages : 512
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Book Description
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.
Author: Lewis Tunstall
Publisher: "O'Reilly Media, Inc."
ISBN: 1098136764
Category : Computers
Languages : en
Pages : 409
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Book Description
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
Author: Aarushi Kansal
Publisher: Springer Nature
ISBN:
Category :
Languages : en
Pages : 175
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Book Description
Author: Erin Pangilinan
Publisher: "O'Reilly Media, Inc."
ISBN: 1492044148
Category : Computers
Languages : en
Pages : 373
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Book Description
Despite popular forays into augmented and virtual reality in recent years, spatial computing still sits on the cusp of mainstream use. Developers, artists, and designers looking to enter this field today have few places to turn for expert guidance. In this book, Erin Pangilinan, Steve Lukas, and Vasanth Mohan examine the AR and VR development pipeline and provide hands-on practice to help you hone your skills. Through step-by-step tutorials, you’ll learn how to build practical applications and experiences grounded in theory and backed by industry use cases. In each section of the book, industry specialists, including Timoni West, Victor Prisacariu, and Nicolas Meuleau, join the authors to explain the technology behind spatial computing. In three parts, this book covers: Art and design: Explore spatial computing and design interactions, human-centered interaction and sensory design, and content creation tools for digital art Technical development: Examine differences between ARKit, ARCore, and spatial mapping-based systems; learn approaches to cross-platform development on head-mounted displays Use cases: Learn how data and machine learning visualization and AI work in spatial computing, training, sports, health, and other enterprise applications
Author: Bolakale Aremu
Publisher: AB Publisher LLC
ISBN:
Category : Computers
Languages : en
Pages : 158
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Book Description
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.
Author: Carlos Rodriguez
Publisher: Packt Publishing Ltd
ISBN: 1835464912
Category : Computers
Languages : en
Pages : 190
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Book Description
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.
Author: Savaş Yıldırım
Publisher: Packt Publishing Ltd
ISBN: 1801078890
Category : Computers
Languages : en
Pages : 374
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Book Description
Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.What you will learn Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability Who this book is for This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.
