Machine Learning with PyTorch and Scikit-Learn

Machine Learning with PyTorch and Scikit-Learn
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
Total Pages: 775
Release: 2022-02-25
Genre: Computers
ISBN: 1801816387
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This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Large Language Models ( LLMs)

Large Language Models ( LLMs)
Author: Maria Johnsen
Publisher: Maria Johnsen
Total Pages: 451
Release: 2024-06-15
Genre: Computers
ISBN:
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This book offers an in-depth exploration of the world of Artificial Intelligence (AI) and Natural Language Processing (NLP), with a special focus on Large Language Models (LLMs). It is designed with academics in mind, making it a perfect resource for students and researchers. Starting with a foundational introduction to AI and its subfields, the book traces the evolution of NLP from rule-based systems to advanced neural networks. It explains the core concepts and architecture of neural networks, highlighting the transformative impact of transformers and attention mechanisms—crucial components for understanding how LLMs process natural language. Detailed explanations of encoder-decoder structures, positional encoding, and various types of neural networks provide a solid technical grounding. A significant portion of the book is dedicated to the practical aspects of working with LLMs. It covers data collection and preprocessing techniques, training objectives, optimization algorithms, and methods for scaling up models. The transition from GPT-2 to GPT-4 is used as a case study to illustrate the computational challenges and advancements in the field. The applications of LLMs are explored across various industries, showcasing their impact on customer service, content creation, journalism, healthcare, and education. Additionally, the book delves into the integration of text with other modalities in multimodal models and the capabilities of zero-shot and few-shot learning. Ethical considerations are a key focus, with discussions on understanding and mitigating bias in LLMs, ensuring responsible AI use, and addressing regulatory and legal implications. The future of LLMs is also contemplated, with predictions for emerging trends and technologies. To provide practical guidance, the book includes chapters on setting up the environment, building and optimizing simple language models, and deploying LLMs in production. It concludes with recommendations for further reading and resources, encouraging continuous learning in this rapidly evolving field. "Large Language Models (LLM)" is a comprehensive resource for anyone interested in understanding, developing, and applying LLMs, from beginners to advanced practitioners. Students are encouraged to buy this book to deepen their knowledge and enhance their academic pursuits.

Application of Large Language Models (LLMs) for Software Vulnerability Detection

Application of Large Language Models (LLMs) for Software Vulnerability Detection
Author: Omar, Marwan
Publisher: IGI Global
Total Pages: 534
Release: 2024-11-01
Genre: Computers
ISBN:
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Large Language Models (LLMs) are redefining the landscape of cybersecurity, offering innovative methods for detecting software vulnerabilities. By applying advanced AI techniques to identify and predict weaknesses in software code, including zero-day exploits and complex malware, LLMs provide a proactive approach to securing digital environments. This integration of AI and cybersecurity presents new possibilities for enhancing software security measures. Application of Large Language Models (LLMs) for Software Vulnerability Detection offers a comprehensive exploration of this groundbreaking field. These chapters are designed to bridge the gap between AI research and practical application in cybersecurity, in order to provide valuable insights for researchers, AI specialists, software developers, and industry professionals. Through real-world examples and actionable strategies, the publication will drive innovation in vulnerability detection and set new standards for leveraging AI in cybersecurity.

Large Language Models

Large Language Models
Author: Oswald Campesato
Publisher: Stylus Publishing, LLC
Total Pages: 517
Release: 2024-09-17
Genre: Computers
ISBN: 1501520601
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This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, Meta AI, Claude 3, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher. FEATURES: Covers in-depth explanations of foundational and advanced LLM concepts, including BERT, GPT-4, and prompt engineering Uses practical Python code samples in leveraging LLM functionalities effectively Discusses future trends, ethical considerations, and the evolving landscape of AI technologies Includes companion files with code, datasets, and images from the book -- available from the publisher for downloading (with proof of purchase)

Hands-On Large Language Models

Hands-On Large Language Models
Author: Jay Alammar
Publisher: "O'Reilly Media, Inc."
Total Pages: 449
Release: 2024-09-11
Genre: Computers
ISBN: 1098150929
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AI has acquired startling new language capabilities in just the past few years. Driven by the rapid advances in deep learning, language AI systems are able to write and understand text better than ever before. This trend enables the rise of new features, products, and entire industries. With this book, Python developers will learn the practical tools and concepts they need to use these capabilities today. You'll learn how to use the power of pre-trained large language models for use cases like copywriting and summarization; create semantic search systems that go beyond keyword matching; build systems that classify and cluster text to enable scalable understanding of large amounts of text documents; and use existing libraries and pre-trained models for text classification, search, and clusterings. This book also shows you how to: Build advanced LLM pipelines to cluster text documents and explore the topics they belong to Build semantic search engines that go beyond keyword search with methods like dense retrieval and rerankers Learn various use cases where these models can provide value Understand the architecture of underlying Transformer models like BERT and GPT Get a deeper understanding of how LLMs are trained Understanding how different methods of fine-tuning optimize LLMs for specific applications (generative model fine-tuning, contrastive fine-tuning, in-context learning, etc.)

Mastering Large Language Models with Python

Mastering Large Language Models with Python
Author: Raj Arun R
Publisher: Orange Education Pvt Ltd
Total Pages: 547
Release: 2024-04-12
Genre: Computers
ISBN: 8197081824
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A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index

A Beginner's Guide to Large Language Models

A Beginner's Guide to Large Language Models
Author: Enamul Haque
Publisher: Enamul Haque
Total Pages: 259
Release: 2024-07-25
Genre: Computers
ISBN: 1445263289
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A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts Step into the revolutionary world of artificial intelligence with "A Beginner's Guide to Large Language Models: Conversational AI for Non-Technical Enthusiasts." Whether you're a curious individual or a professional seeking to leverage AI in your field, this book demystifies the complexities of large language models (LLMs) with engaging, easy-to-understand explanations and practical insights. Explore the fascinating journey of AI from its early roots to the cutting-edge advancements that power today's conversational AI systems. Discover how LLMs, like ChatGPT and Google's Gemini, are transforming industries, enhancing productivity, and sparking creativity across the globe. With the guidance of this comprehensive and accessible guide, you'll gain a solid understanding of how LLMs work, their real-world applications, and the ethical considerations they entail. Packed with vivid examples, hands-on exercises, and real-life scenarios, this book will empower you to harness the full potential of LLMs. Learn to generate creative content, translate languages in real-time, summarise complex information, and even develop AI-powered applications—all without needing a technical background. You'll also find valuable insights into the evolving job landscape, equipping you with the knowledge to pursue a successful career in this dynamic field. This guide ensures that AI is not just an abstract concept but a tangible tool you can use to transform your everyday life and work. Dive into the future with confidence and curiosity, and discover the incredible possibilities that large language models offer. Join the AI revolution and unlock the secrets of the technology that's reshaping our world. "A Beginner's Guide to Large Language Models" is your key to understanding and mastering the power of conversational AI. Introduction This introduction sets the stage for understanding the evolution of artificial intelligence (AI) and large language models (LLMs). It highlights the promise of making complex AI concepts accessible to non-technical readers and outlines the unique approach of this book. Chapter 1: Demystifying AI and LLMs: A Journey Through Time This chapter introduces the basics of AI, using simple analogies and real-world examples. It traces the evolution of AI, from rule-based systems to machine learning and deep learning, leading to the emergence of LLMs. Key concepts such as tokens, vocabulary, and embeddings are explained to build a solid foundation for understanding how LLMs process and generate language. Chapter 2: Mastering Large Language Models Delving deeper into the mechanics of LLMs, this chapter covers the transformer architecture, attention mechanisms, and the processes involved in training and fine-tuning LLMs. It includes hands-on exercises with prompts and discusses advanced techniques like chain-of-thought prompting and prompt chaining to optimise LLM performance. Chapter 3: The LLM Toolbox: Unleashing the Power of Language AI This chapter explores the diverse applications of LLMs in text generation, language translation, summarisation, question answering, and code generation. It also introduces multimodal LLMs that handle both text and images, showcasing their impact on various creative and professional fields. Practical examples and real-life scenarios illustrate how these tools can enhance productivity and creativity. Chapter 4: LLMs in the Real World: Transforming Industries Highlighting the transformative impact of LLMs across different industries, this chapter covers their role in healthcare, finance, education, creative industries, and business. It discusses how LLMs are revolutionising tasks such as medical diagnosis, fraud detection, personalised tutoring, and content creation, and explores the future of work in an AI-powered world. Chapter 5: The Dark Side of LLMs: Ethical Concerns and Challenges Addressing the ethical challenges of LLMs, this chapter covers bias and fairness, privacy concerns, misuse of LLMs, security threats, and the transparency of AI decision-making. It also discusses ethical frameworks for responsible AI development and presents diverse perspectives on the risks and benefits of LLMs. Chapter 6: Mastering LLMs: Advanced Techniques and Strategies This chapter focuses on advanced techniques for leveraging LLMs, such as combining transformers with other AI models, fine-tuning open-source LLMs for specific tasks, and building LLM-powered applications. It provides detailed guidance on prompt engineering for various applications and includes a step-by-step guide to creating an AI-powered chatbot. Chapter 7: LLMs and the Future: A Glimpse into Tomorrow Looking ahead, this chapter explores emerging trends and potential breakthroughs in AI and LLM research. It discusses ethical AI development, insights from leading AI experts, and visions of a future where LLMs are integrated into everyday life. The chapter highlights the importance of building responsible AI systems that address societal concerns. Chapter 8: Your LLM Career Roadmap: Navigating the AI Job Landscape Focusing on the growing demand for LLM expertise, this chapter outlines various career paths in the AI field, such as LLM scientists, engineers, and prompt engineers. It provides resources for building the necessary skillsets and discusses the evolving job market, emphasising the importance of continuous learning and adaptability in a rapidly changing industry. Thought-Provoking Questions, Simple Exercises, and Real-Life Scenarios The book concludes with practical exercises and real-life scenarios to help readers apply their knowledge of LLMs. It includes thought-provoking questions to deepen understanding and provides resources and tools for further exploration of LLM applications. Tools to Help with Your Exercises This section lists tools and platforms for engaging with LLM exercises, such as OpenAI's Playground, Google Translate, and various IDEs for coding. Links to these tools are provided to facilitate hands-on learning and experimentation.

Machine Learning Design Patterns

Machine Learning Design Patterns
Author: Valliappa Lakshmanan
Publisher: O'Reilly Media
Total Pages: 408
Release: 2020-10-15
Genre: Computers
ISBN: 1098115759
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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Generative AI and Large Language Models

Generative AI and Large Language Models
Author: Aditya Pratap Bhuyan
Publisher: Aditya Pratap Bhuyan
Total Pages: 109
Release: 2024-07-24
Genre: Computers
ISBN:
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Artificial Intelligence is reshaping our world, and at the forefront of this revolution are Generative AI and Large Language Models (LLMs). This book, "Generative AI and Large Language Models: Revolutionizing the Future," offers an in-depth exploration of these groundbreaking technologies, delving into their foundations, development, and profound implications for various industries and society as a whole. Starting with a historical overview of AI, the book traces the evolution of machine learning and deep learning, setting the stage for understanding the rise of generative AI. Readers will discover the inner workings of LLMs, from their advanced neural network architectures to the massive datasets and computational power required for their training. Key models, such as the Generative Pre-trained Transformer (GPT) series, are examined in detail, showcasing their remarkable capabilities in natural language processing and beyond. The book also addresses the ethical and social challenges posed by these powerful technologies. Issues such as bias, fairness, and privacy are discussed, alongside the need for transparent and accountable AI systems. Through real-world applications and case studies, readers will see how generative AI is transforming fields like healthcare, finance, content creation, and more. Looking ahead, the book explores future trends and innovations, highlighting potential advancements and the ongoing research aimed at enhancing AI's efficiency and multimodal capabilities. It envisions a future where AI and humans collaborate more closely, driving progress and innovation across all domains. "Generative AI and Large Language Models: Revolutionizing the Future" is an essential read for anyone interested in the cutting-edge of AI technology. Whether you are a researcher, practitioner, or simply curious about the future of AI, this book provides a comprehensive and accessible guide to the transformative power of generative AI and LLMs.

Challenges in Large Language Model Development and AI Ethics

Challenges in Large Language Model Development and AI Ethics
Author: Gupta, Brij
Publisher: IGI Global
Total Pages: 521
Release: 2024-08-15
Genre: Computers
ISBN:
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The development of large language models has resulted in artificial intelligence advancements promising transformations and benefits across various industries and sectors. However, this progress is not without its challenges. The scale and complexity of these models pose significant technical hurdles, including issues related to bias, transparency, and data privacy. As these models integrate into decision-making processes, ethical concerns about their societal impact, such as potential job displacement or harmful stereotype reinforcement, become more urgent. Addressing these challenges requires a collaborative effort from business owners, computer engineers, policymakers, and sociologists. Fostering effective research for solutions to address AI ethical challenges may ensure that large language model developments benefit society in a positive way. Challenges in Large Language Model Development and AI Ethics addresses complex ethical dilemmas and challenges of the development of large language models and artificial intelligence. It analyzes ethical considerations involved in the design and implementation of large language models, while exploring aspects like bias, accountability, privacy, and social impacts. This book covers topics such as law and policy, model architecture, and machine learning, and is a useful resource for computer engineers, sociologists, policymakers, business owners, academicians, researchers, and scientists.