Search results for "python for generative ai"
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Python Debugging for AI, Machine Learning, and Cloud Computing
This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior.The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, you’ll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You’ll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performance degradation. This includes tracing, logging, and analyziing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.What You Will LearnEmploy a pattern-oriented approach to Python debugging that starts with diagnostics of common software problemsUse tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debuggingUnderstand Python internals for interfacing with operating systems and external modulesPerform Python memory dump analysis, tracing, and loggingWho This Book Is ForSoftware developers, AI/ML engineers, researchers, data engineers, as well as MLOps and DevOps professionals.
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Blueprints for Text Analytics using Python
Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.Extract data from APIs and web pagesPrepare textual data for statistical analysis and machine learningUse machine learning for classification, topic modeling, and summarizationExplain AI models and classification resultsExplore and visualize semantic similarities with word embeddingsIdentify customer sentiment in product reviewsCreate a knowledge graph based on named entities and their relations
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Understanding the Ethical Concerns of Generative AI
This textbook is designed to help readers navigate a world increasingly shaped by generative AI technologies. It prepares readers to confront issues such as bias, sustainability, transparency, and privacy as these technologies become more ubiquitous in daily life. Emphasis is placed on developing a theoretical foundation for ethical behavior and understanding the new ethical concerns that emerge from the use of generative AI.
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AI-Ready Data Blueprints
Companies innovating with generative AI understand that having the right data foundation is critical for success and profitability. To best position themselves for long-term success, organizations must prioritize investments in data and AI governance. AI-Ready Data Blueprints is your map to connecting data strategy, GenAI, and ethical practices to build and scale truly effective solutions. Taking a comprehensive, cloud-agnostic approach focused on real-world business challenges, seasoned data and AI experts Navnit Shukla, Kien Pham, Srikanth Sopirala, and Harsha Tadiparthi share actionable insights to guide you in designing and implementing effective data-centric GenAI systems. Whether you're new to GenAI or are already focusing on optimizing it for accuracy, speed, or both, the principles shared in this book will empower you to excel in all your AI endeavors. Identify the key elements of a solid data foundation for generative AIApply data governance and orchestration techniques to ensure high data quality, access control, and proper data lineage for reliable AI systemsOptimize GenAI applications through prompt engineering, fine-tuning, and retrieval-augmented generationImplement security, compliance, and governance measures, including responsible AI practices, transparency, and more
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Generative AI for a Net-Zero Economy
This book covers the technological aspects of Generative AI, its applications in achieving a net-zero carbon economy, the challenges of climate change, and the economic and management implications of these transitions. The book explores the transformative potential of Generative AI in driving the transition to a net-zero economy. It examines how this cutting-edge technology is revolutionizing climate change mitigation strategies and reshaping business models in the digital age. The book offers a comprehensive guide to leveraging AI for sustainable innovation through case studies, expert insights, and forward-thinking analysis. It addresses the challenges and opportunities in integrating AI into climate action plans, economic policies, and corporate strategies. Essential reading for policymakers, business leaders, and technologists, this book provides a roadmap for harnessing Generative AI to create a sustainable, prosperous future encompassing several key elements that align with the United Nations Sustainable Development Goals, in particular SDG 13: Climate Action.
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Generative AI and Digital Forensics
In today’s world, cybersecurity attacks and security breaches are becoming the norm. Unfortunately, we are not immune to it, and any individual and entity is at dire risk. The best and only thing that we can do is to mitigate the risks as much as much as possible so that they do not happen at all. But even when a security breach does indeed happen, the immediate reaction is to contain it so that it does not penetrate further into the IT/Network Infrastructure. From there, mission-critical processes need to be restored, until the business can resume a normal state of operations, like it was before the security breach.But another key step here is to investigate how and why the security breach happened in the first place. The best to do this is through what is known as “Digital Forensics”. This is where specially trained Digital Forensics Investigators collect and comb through every piece of evidence to determine this. Eventually, the goal is then to use this evidence in a court of law so the cyberattacker can be made to answer to their crime and eventually be brought to justice.However, the area of digital forensics is a large one, and many topics around it can be covered. Also, generative AI is being used to not only help in the analysis of the evidence, but also to help automate the Digital Forensics software packages that are available today. Therefore, in this book, the following is covered:Examples of security breaches, and overview into Digital ForensicsHow Digital Forensics can be used to investigate the loss or theft of dataAn introduction to the SQL Server DatabaseA review of the SQL Injection AttackHow generative AI can be used in Digital Forensics
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Python for Tensor Flow
As part of the best-selling Pocket Primer series, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics. It begins with a quick introduction to Python, followed by chapters that discuss NumPy, Pandas, Matplotlib, and scikit-learn. The final two chapters contain an assortment of TensorFlow 1.x code samples, including detailed code samples for TensorFlow Dataset (which is used heavily in TensorFlow 2 as well). A TensorFlow Dataset refers to the classes in the tf.data.Dataset namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e.g., map(), filter(), batch(), and so forth, based on data from one or more data sources. Companion files with source code are available for downloading from the publisher. FEATURES, A practical introduction to Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow 1.x, Contains relevant NumPy/Pandas code samples that are typical in machine learning topics, and also useful TensorFlow 1.x code samples for deep learning/TensorFlow topics, Includes many examples of TensorFlow Dataset APIs with lazy operators, e.g., map(), filter(), batch(), take() and also method chaining such operators, Assumes the reader has very limited experience, Includes companion files with all of the source code examples (download from the publisher).
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Combating Misinformation in the Age of Generative AI
The rapid advancement of Generative AI (GenAI) is revolutionizing and transforming a wide range of fields, with a notable impact on digital media and information dissemination. Digital platforms, including social media networks, have already grappled with the widespread proliferation of fake news at an increasing intensity are now facing an even bigger threat from machine-generated and machine-boosted fake news. With the increasing growth and sophistication of GenAI models for malicious ends, fake news is spreading rapidly, finding a fertile ground in digital media for largely unchecked dissemination of incorrect and misleading information. The scalability and diversity of current and emerging GenAI techniques present both opportunities and challenges in the field of fake news. On one hand, the ability to create highly realistic and persuasive content in large quantities with minimal effort, in real-time, makes GenAI a potent tool for spreading fake news and misinformation. On the other hand, those same capabilities can be harnessed to employ GenAI models to detect fake news and therefore hinder the spread. In this context, this book examines how GenAI techniques are contributing to combat fake news in digital media. A particular major challenge in combating fake news with GenAI is the explainability of the models: why a specific piece of news is determined to be fake or genuine. Tracing the origins of a specific piece of generated content—whether it stems from accurate or misleading data—requires extracting, interpreting, and representing in a legible manner vast parameter spaces and training data sources. This complexity of GenAI explainability makes it difficult to hold creators of fake news accountable and to ensure transparency in the content creation process. In this book, the authors focus on the positive potential of Generative AI and provide an overview of how it can be leveraged to combat fake news across diverse digital media platforms.The book is structured around the following key topics:• An overview of Generative AI and its relationship with fake news• How Generative AI can be utilized to fight the spread of fake news• Ensuring that the news we encounter on digital media platforms are not misleading or false• Methods for verifying the authenticity of news sources and tracing the origins of information to ensure its credibility• Practical insights into technological advancements and solutions related to the detection and analysis of fake news.Finally, this book serves as a platform for the community to share innovative research on the use of Generative AI in detecting and mitigating the impact of false information across digital platforms. Readers will explore how Generative AI can revolutionize fact-checking, automate the identification of misleading content, and provide adaptive solutions to prevent the spread of fake news.
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Gen AI-assisted Agile Course, Project-Based Teaching and Learning
This title represents an essential resource for understanding the evolving landscape of AI-augmented pedagogy, examining the transformative effects of generative artificial intelligence (GenAI) on education through the lens of agile course design and project-based learning.Through a detailed analysis of diverse empirical case studies from eight countries between 2015 and 2024, the authors effectively illustrate the dual impact of GenAI on education, academic integrity, and teacher autonomy. They demonstrate how GenAI can enhance personalized STEM education and STREAM (Science, Technology, Reading and Writing, Engineering, Arts and Mathematics) instruction, as well as project-based learning, while simultaneously presenting significant challenges. They provide a nuanced assessment of the opportunities and disruptions introduced by GenAI and offer concrete pedagogical strategies for teaching practitioners.This book serves as a vital reference for scholars in GenAI, educational technology and curriculum, and instruction. It also provides practical, ready-to-implement pedagogical approaches for pre-service teachers in higher education and primary and secondary schools.
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Creative Prototyping with Generative AI
Reimagine different generative AI as useful creative prototyping tools that can be used to augment your own creative process and projects. Gain a deeper understanding of how generative AI can elevate your creative future. You will acquire a comprehensive understanding of how AI works, uncover tools that can enhance your AI interactions, learn how to extract maximum potential from AI-produced content, and experiment with methods for assessing, refining, and boosting the content to transform your creative projects. You'll also explore how creative professionals from varied disciplines are employing generative AI in their workflows to produce distinctive contributions to the world. Each chapter provides examples of how designers and other creative individuals can utilize these technological wonders, adopting various prototyping techniques to fast-track and optimize design processes and workflows. Creators from all disciplines can tap into the vast capabilities and benefits of generative AI, enabling them to rapidly experiment and prototype their ideas.You Will Learn:Understand how generative AI can support your own creative processLearn tools to get the most out of text-text, text-image, and text-video generative AIAugment your design practices using generative AIDraw inspiration from AI generated content to create unique creative workImprove and streamline creatives processes and workflowsWho This Book Is ForDigital media professionals who want to access off-the shelf creative tools to improve and accelerate their creativity and workflow.Designers and engineers who are looking at novel ways to improve their prototyping and testing processes.Students who want to use AI to rapidly generate ideas to support them in prototyping assignments.Instructors interested in pointing their students to a variety of accessible AI resources to manage their own creativity.
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Artificial Intelligence in Action
This comprehensive book dives deep into the current landscape of AI, exploring its fundamental principles, development challenges, potential risks, and the cutting-edge breakthroughs that are propelling it forward. Artificial intelligence (AI) is rapidly transforming industries and societies worldwide through groundbreaking innovations and real-world applications. Starting with the core concepts, the book examines the various types of AI systems, generative AI models, and the complexities of machine learning. It delves into the programming languages driving AI development, data pipelines, model creation and deployment processes, while shedding light on issues like AI hallucinations and the intricate path of machine unlearning.The book then showcases the remarkable real-world applications of AI across diverse domains. From preventing job displacement and promoting environmental sustainability, to enhancing disaster response, drone technology, and even nuclear energy innovation, it highlights how AI is tackling complex challenges and driving positive change.The book also explores the double-edged nature of AI, recognizing its tremendous potential while cautioning about the risks of misuse, unintended consequences, and the urgent need for responsible development practices. It examines the intersection of AI and fields like operating system design, warfare, and semiconductor technology, underscoring the wide-ranging implications of this transformative force.As the quest for artificial general intelligence (AGI) and superintelligent AI systems intensifies, the book delves into cutting-edge research, emerging trends, and the pursuit of multimodal, explainable, and causally aware AI systems. It explores the symbiotic relationship between AI and human creativity, the rise of user-friendly "casual AI," and the potential of AI to tackle open-ended tasks.This is an essential guide for understanding the profound impact of AI on our world today and its potential to shape our future. From the frontiers of innovation to the challenges of responsible development, this book offers a comprehensive and insightful exploration of the remarkable real-world applications and innovations driving the AI revolution.
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