As artificial intelligence (AI) becomes more and more woven into our everyday lives—and underpins so much of the infrastructure we rely on—the ethical, security, and privacy implications require a critical approach that draws not simply on the programming and algorithmic foundations of the technology. Bringing together legal studies, philosophy, cybersecurity, and academic literature, Beyond the Algorithm examines these complex issues with a comprehensive, easy-to-understand analysis and overview. The book explores the ethical challenges that professionals—and, increasingly, users—are encountering as AI becomes not just a promise of the future, but a powerful tool of the present. An overview of the history and development of AI, from the earliest pioneers in machine learning to current applications and how it might shape the future Introduction to AI models and implementations, as well as examples of emerging AI trends Examination of vulnerabilities, including insight into potential real-world threats, and best practices for ensuring a safe AI deployment Discussion of how to balance accountability, privacy, and ethics with regulatory and legislative concerns with advancing AI technology A critical perspective on regulatory obligations, and repercussions, of AI with copyright protection, patent rights, and other intellectual property dilemmas An academic resource and guide for the evolving technical and intellectual challenges of AI Leading figures in the field bring to life the ethical issues associated with AI through in-depth analysis and case studies in this comprehensive examination.

In the ever-evolving world of Natural Language Processing (NLP), "Master NLP with Hugging Face: A Fine-tuning Toolkit" equips you to unlock the power of pre-trained models from Hugging Face. This comprehensive guide empowers you to transform these powerful models into workhorses for your specific NLP tasks. Gone are the days of training complex NLP models from scratch. This book dives into the art of fine-tuning, a technique that leverages the vast knowledge pre-trained models have already acquired and tailors it to your specific needs. You'll delve into the fundamentals of fine-tuning, understanding how to take a pre-trained model and adjust its final layers to excel on your chosen NLP task, whether it's text classification, sentiment analysis, question answering, or summarization. The book doesn't just provide theory - it's a hands-on toolkit. You'll establish your NLP development environment, ensuring you have the necessary tools to get started. By following step-by-step guides, you'll navigate the treasure trove of pre-trained models on the Hugging Face Model Hub, selecting the perfect model for your project. Data is the fuel for fine-tuning, and this book equips you to prepare your data effectively. Learn essential data cleaning and pre-processing techniques to ensure your model receives high-quality input. Master the art of data splitting, creating distinct training, validation, and test sets to optimize your model's performance and generalization capabilities. As you venture into fine-tuning, the book equips you to tackle challenges like overfitting and data requirements. Explore techniques to mitigate these issues and ensure your fine-tuned model performs exceptionally well on unseen data. Moving beyond the basics, "Master NLP with Hugging Face" introduces you to advanced concepts like building custom pipelines for text processing and customizing training configurations for optimal performance. You'll also gain insights into evaluation metrics, allowing you to precisely measure the effectiveness of your fine-tuned model for your specific NLP task. This book is your gateway to the exciting world of fine-tuning Hugging Face Transformers. With its comprehensive guidance and practical approach, you'll be well on your way to building robust and efficient NLP applications that can handle real-world challenges.

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.

Speed up your testing and deliver exceptional product quality with the power of AI tools. The more you test, the more you learn about your software. Software Testing with Generative AI shows you how you can expand, automate, and enhance your testing with Large Language Model (LLM)-based AI. Your team will soon be delivering higher quality tests, all in less time. In Software Testing with Generative AI you’ll learn how to: • Spot opportunities to improve test quality with AI • Construct test automation with the support of AI tools • Formulate new ideas during exploratory testing using AI tools • Use AI tools to aid the design process of new features • Improve the testability of a context with the help of AI tools • Maximize your output with prompt engineering • Create custom LLMs for your business’s specific needs Software Testing with Generative AI is full of hype-free advice for supporting your software testing with AI. In it, you’ll find strategies from bestselling author Mark Winteringham to generate synthetic testing data, implement automation, and even augment and improve your test design with AI. Foreword by Nicola Martin. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology There’s a simple rule in software testing: the more you test, the more you learn. And as any testing pro will tell you, good testing takes time. By integrating large language models (LLMs) and generative AI into your process, you can dramatically automate and enhance testing, improve quality and coverage, and deliver more meaningful results. About the book Software Testing with Generative AI shows you how AI can elevate every aspect of testing—automation, test data management, test scripting, exploratory testing, and more! Learn how to use AI coding tools like Copilot to guide test-driven development, get relevant feedback about your applications from ChatGPT, and use the OpenAI API to integrate AI into your data generation. You’ll soon have higher-quality testing that takes up less of your time. What's inside • Improve test quality and coverage • AI-powered test automation • Build agents that act as testing assistants About the reader For developers, testers, and quality engineers. About the author Mark Winteringham is an experienced software tester who teaches many aspects of software testing. He is the author of Testing Web APIs. The technical editor on this book was Robert Walsh. Table of Contents Part 1 1 Enhancing testing with large language models 2 Large language models and prompt engineering 3 Artificial intelligence, automation, and testing Part 2 4 AI-assisted testing for developers 5 Test planning with AI support 6 Rapid data creation using AI 7 Accelerating and improving UI automation using AI 8 Assisting exploratory testing with artificial intelligence 9 AI agents as testing assistants Part 3 10 Introducing customized LLMs 11 Contextualizing prompts with retrieval-augmented generation 12 Fine-tuning LLMs with business domain knowledge Appendix A Setting up and using ChatGPT Appendix B Setting up and using GitHub Copilot Appendix C Exploratory testing notes

Your key to transformer based NLP, vision, speech, and multimodalities KEY FEATURES ● Transformer architecture for different modalities and multimodalities. ● Practical guidelines to build and fine-tune transformer models. ● Comprehensive code samples with detailed documentation. DESCRIPTION This book covers transformer architecture for various applications including NLP, computer vision, speech processing, and predictive modeling with tabular data. It is a valuable resource for anyone looking to harness the power of transformer architecture in their machine learning projects. The book provides a step-by-step guide to building transformer models from scratch and fine-tuning pre-trained open-source models. It explores foundational model architecture, including GPT, VIT, Whisper, TabTransformer, Stable Diffusion, and the core principles for solving various problems with transformers. The book also covers transfer learning, model training, and fine-tuning, and discusses how to utilize recent models from Hugging Face. Additionally, the book explores advanced topics such as model benchmarking, multimodal learning, reinforcement learning, and deploying and serving transformer models. In conclusion, this book offers a comprehensive and thorough guide to transformer models and their various applications. WHAT YOU WILL LEARN ● Understand the core architecture of various foundational models, including single and multimodalities. ● Step-by-step approach to developing transformer-based Machine Learning models. ● Utilize various open-source models to solve your business problems. ● Train and fine-tune various open-source models using PyTorch 2.0 and the Hugging Face ecosystem. ● Deploy and serve transformer models. ● Best practices and guidelines for building transformer-based models. WHO THIS BOOK IS FOR This book caters to data scientists, Machine Learning engineers, developers, and software architects interested in the world of generative AI. TABLE OF CONTENTS 1. Transformer Architecture 2. Hugging Face Ecosystem 3. Transformer Model in PyTorch 4. Transfer Learning with PyTorch and Hugging Face 5. Large Language Models: BERT, GPT-3, and BART 6. NLP Tasks with Transformers 7. CV Model Anatomy: ViT, DETR, and DeiT 8. Computer Vision Tasks with Transformers 9. Speech Processing Model Anatomy: Whisper, SpeechT5, and Wav2Vec 10. Speech Tasks with Transformers 11. Transformer Architecture for Tabular Data Processing 12. Transformers for Tabular Data Regression and Classification 13. Multimodal Transformers, Architectures and Applications 14. Explore Reinforcement Learning for Transformer 15. Model Export, Serving, and Deployment 16. Transformer Model Interpretability, and Experimental Visualization 17. PyTorch Models: Best Practices and Debugging

Use LLMs to build better business software applications Autonomously communicate with users and optimize business tasks with applications built to make the interaction between humans and computers smooth and natural. Artificial Intelligence expert Francesco Esposito illustrates several scenarios for which a LLM is effective: crafting sophisticated business solutions, shortening the gap between humans and software-equipped machines, and building powerful reasoning engines. Insight into prompting and conversational programming—with specific techniques for patterns and frameworks—unlock how natural language can also lead to a new, advanced approach to coding. Concrete end-to-end demonstrations (featuring Python and ASP.NET Core) showcase versatile patterns of interaction between existing processes, APIs, data, and human input. Artificial Intelligence expert Francesco Esposito helps you: Understand the history of large language models and conversational programming Apply prompting as a new way of coding Learn core prompting techniques and fundamental use-cases Engineer advanced prompts, including connecting LLMs to data and function calling to build reasoning engines Use natural language in code to define workflows and orchestrate existing APIs Master external LLM frameworks Evaluate responsible AI security, privacy, and accuracy concerns Explore the AI regulatory landscape Build and implement a personal assistant Apply a retrieval augmented generation (RAG) pattern to formulate responses based on a knowledge base Construct a conversational user interface For IT Professionals and Consultants For software professionals, architects, lead developers, programmers, and Machine Learning enthusiasts For anyone else interested in natural language processing or real-world applications of human-like language in software