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Author: Omar Sanseviero Publisher: ISBN: 9781098149246 Category : Computers Languages : en Pages : 0
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
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: Carlos Rodriguez Publisher: Packt Publishing Ltd ISBN: 1835464912 Category : Computers Languages : en Pages : 190
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: Publisher: Anand Vemula ISBN: Category : Antiques & Collectibles Languages : en Pages : 111
Book Description
" This guide is crafted for those interested in leveraging Rust's performance and safety features to build innovative generative AI models. Starting with the basics, the book covers essential models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), providing clear, practical examples that demonstrate their implementation in Rust. As the book progresses, it delves into more sophisticated topics, including advanced model architectures like transformers and diffusion models. It also covers critical optimization techniques, ensuring that your AI models are both efficient and effective. The ethical aspects of AI development are thoroughly discussed, with practical advice on how to address common pitfalls such as bias and misinformation. This book is packed with hands-on exercises, from constructing full AI pipelines to applying Rust in real-world scenarios such as AI-driven art and content generation. By the conclusion, readers will have gained a solid understanding of how to utilize Rust for building and deploying generative AI models across a variety of applications. "
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
Practical Generative AI for Data Science: From Theory to Real-World Applications" is a comprehensive guide that bridges the gap between theory and practical implementation of generative AI techniques in the field of data science. This book equips readers with essential knowledge and hands-on skills to effectively harness the power of generative models for diverse applications. Starting with foundational concepts, the book introduces readers to various types of generative models, including Gaussian Mixture Models, Hidden Markov Models, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and more. Each model is explained with clear examples, use cases, and case studies drawn from industries such as finance, healthcare, and media. The practical implementation section provides step-by-step tutorials and complete code solutions using popular frameworks like TensorFlow and PyTorch. Readers learn how to build and train models for tasks such as image generation, natural language processing (NLP), anomaly detection, and speech synthesis. Detailed explanations of model architectures, optimization techniques, and evaluation metrics ensure a deep understanding of each concept. Furthermore, the book addresses advanced topics including conditional generative models, sequential generative models like RNNs and Transformers, energy-based models, and diffusion models. These chapters delve into cutting-edge research, emerging trends, and practical applications across various industries. Ethical considerations and regulatory concerns associated with generative AI are also discussed, emphasizing the importance of fairness, transparency, and privacy in model development and deployment. "Practical Generative AI for Data Science" is an indispensable resource for data scientists, machine learning engineers, and researchers looking to leverage generative AI for solving real-world problems. Whether you are new to generative models or seeking to deepen your expertise, this book provides the knowledge and tools needed to succeed in the rapidly evolving field of AI.
Author: Mark Liu Publisher: Manning ISBN: 9781633436466 Category : Computers Languages : en Pages : 0
Book Description
Create your own generative AI models for text, images, music, and more! Generative AI tools like ChatGPT, Bard, and DALL-E have transformed the way we work. Learn Generative AI with PyTorch takes you on an incredible hands-on journey through creating and training AI models using Python, the free PyTorch framework and the hardware you already have in your office. Along the way, you’ll master the fundamentals of General Adversarial Networks (GANs), Transformers, Large Language Models (LLMs), variational autoencoders, diffusion models, LangChain, and more! In Learn Generative AI with PyTorch you’ll build these amazing models: A simple English-to-French translator A text-generating model as powerful as GPT-2 A diffusion model that produces realistic flower images Music generators using GANs and Transformers An image style transfer model A zero-shot know-it-all agent All you need is Python and the fundamentals of machine learning to get started. You’ll learn the rest as you go! Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the book Learn Generative AI with PyTorch teaches the underlying mechanics of generative AI by building working AI models from scratch. Every model you’ll create is fun and fascinating, in projects that include generating color images of anime faces, changing the hair color in a photograph, training a model to write like Hemingway, and generating music in the style of Mozart. Throughout, you’ll use the intuitive PyTorch framework that’s instantly familiar to anyone who’s worked with Python data tools. You’ll begin by creating simple content like shapes, numbers, and images using Generative Adversarial Networks (GANs). Then, each chapter introduces a new project as you work towards building your own LLMs. About the reader For Python programmers who know the basics of machine learning. No knowledge of PyTorch or generative AI required. About the author Dr. Mark Liu is a tenured finance professor and the founding director of the Master of Science in Finance program at the University of Kentucky. He has more than 20 years of coding experience, a Ph.D. in finance from Boston College.
Author: Jeremy Howard Publisher: O'Reilly Media ISBN: 1492045497 Category : Computers Languages : en Pages : 624
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: Rick Spair Publisher: Rick Spair ISBN: Category : Computers Languages : en Pages : 146
Book Description
Generative AI represents a groundbreaking frontier in the realm of artificial intelligence, where machines not only learn from data but also create new data, mimicking the inventive processes of human creativity. This book is a comprehensive guide that explores the depths of generative AI, from foundational concepts to advanced applications, and provides a rich array of hands-on projects and real-world case studies. Why Generative AI? In recent years, generative AI has transformed from a niche area of research to a central pillar of AI innovation, with profound implications for various industries. From generating realistic images and videos to composing music and writing compelling narratives, generative AI models are pushing the boundaries of what machines can do. This evolution has not only expanded the capabilities of AI but also sparked new forms of creative expression and problem-solving. Generative AI's impact is evident in numerous fields: Art and Design: Artists and designers are leveraging AI to create stunning visual artworks, intricate designs, and immersive digital environments. Tools like DeepDream and GauGAN have opened new horizons in artistic creativity, enabling the generation of unique and surreal visuals. Media and Entertainment: The media industry is using generative AI to automate content creation, from news articles to movie scripts, and even to generate entire virtual worlds for video games and virtual reality experiences. AI-generated music and soundtracks are also becoming increasingly popular, offering new ways to enhance auditory experiences. Healthcare: In healthcare, generative AI is aiding in the discovery of new drugs, personalizing treatment plans, and enhancing medical imaging. By generating realistic simulations and models, AI helps researchers and practitioners explore new avenues in medical science. Business and Marketing: Businesses are employing generative AI to create personalized marketing content, design products, and optimize supply chains. AI-driven tools are enabling companies to innovate faster and more efficiently, providing a competitive edge in the market. Dive into the projects, experiment with different models, and engage with the AI community. By learning, creating, and sharing, you become a part of the vibrant and dynamic landscape of generative AI. The future is filled with opportunities, and this book is your gateway to exploring and contributing to the exciting world of generative AI. Welcome to the journey!
Author: Denis Rothman Publisher: Packt Publishing Ltd ISBN: 1805123742 Category : Computers Languages : en Pages : 731
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: Omar Sanseviero
Author: Omar Sanseviero
Author: MARIJA. JEGOROVA
Author: Carlos Rodriguez
Author: 
Author: Anand Vemula
Author: Mark Liu
Author: Aarushi Kansal
Author: Jeremy Howard
Author: Rick Spair
Author: Denis Rothman