Search results for "Python for Generative AI"
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GPT Meets Game Theory
This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today’s most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way.As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to “mean-field” models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved.This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.
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Learning IPython for Interactive Computing and Data Visualization -
Get started with Python for data analysis and numerical computing in the Jupyter notebookAbout This Book• Learn the basics of Python in the Jupyter Notebook• Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn• Perform highly-efficient numerical computations with Numba, Cython, and ipyparallelWho This Book Is ForThis book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know.What You Will Learn• Install Anaconda and code in Python in the Jupyter Notebook• Load and explore datasets interactively• Perform complex data manipulations effectively with pandas• Create engaging data visualizations with matplotlib and seaborn• Simulate mathematical models with NumPy• Visualize and process images interactively in the Jupyter Notebook with scikit-image• Accelerate your code with Numba, Cython, and IPython.parallel• Extend the Notebook interface with HTML, JavaScript, and D3In DetailPython is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.Style and approachThis is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.
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Mastering Python Scripting for System Administrators
Leverage the features and libraries of Python to administrate your environment efficiently.Key FeaturesLearn how to solve problems of system administrators and automate routine activitiesLearn to handle regular expressions, network administration Building GUI, web-scraping and database administration including data analyticsBook DescriptionPython has evolved over time and extended its features in relation to every possible IT operation. Python is simple to learn, yet has powerful libraries that can be used to build powerful Python scripts for solving real-world problems and automating administrators' routine activities. The objective of this book is to walk through a series of projects that will teach readers Python scripting with each project.This book will initially cover Python installation and quickly revise basic to advanced programming fundamentals. The book will then focus on the development process as a whole, from setup to planning to building different tools. It will include IT administrators' routine activities (text processing, regular expressions, file archiving, and encryption), network administration (socket programming, email handling, the remote controlling of devices using telnet/ssh, and protocols such as SNMP/DHCP), building graphical user interface, working with websites (Apache log file processing, SOAP and REST APIs communication, and web scraping), and database administration (MySQL and similar database data administration, data analytics, and reporting).By the end of this book, you will be able to use the latest features of Python and be able to build powerful tools that will solve challenging, real-world tasksWhat you will learnUnderstand how to install Python and debug Python scriptsUnderstand and write scripts for automating testing and routine administrative activitiesUnderstand how to write scripts for text processing, encryption, decryption, and archivingHandle files, such as pdf, excel, csv, and txt files, and generate reportsWrite scripts for remote network administration, including handling emailsBuild interactive tools using a graphical user interfaceHandle Apache log files, SOAP and REST APIs communicationAutomate database administration and perform statistical analysisWho this book is forThis book would be ideal for users with some basic understanding of Python programming and who are interested in scaling their programming skills to command line scripting and system administration. Prior knowledge of Python would be necessary.
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Generative AI
The cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of Phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks.The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for Phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website.But, Generative AI can also be used for the good also, to combat Phishing Attacks. This is the topic of this book. In this, we cover the following:A review of PhishingA review of AI, Neural Networks, and Machine LearningA review of Natural Language Processing, Generative AI, and the Digital PersonA proposed solution as to how Generative AI can combat Phishing Attacks, as they relate to Privileged Access accounts
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GPT Meets Game Theory
This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today’s most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way.As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to “mean-field” models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved.This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.
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Intro to Python for Computer Science and Data Science
For introductory-level Python programming and/or data-science courses. A groundbreaking, flexible approach to computer science and data science The Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs), and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.
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Generative AI and Education
This volume addresses the gap in knowledge around generative AI and its applications in education. It draws on the recent history of technological innovation and digital pedagogies, locating generative AI in the contemporary discourse around education futures. It argues that a new hybrid model of education is emerging, requiring educational institutions to embed generative AI into course and programme design, delivery and assessment. It also proposes a shift from a focus on learning as output to learning as a process, and explores what that shift might look like. Grounded in educational theory, it offers actionable pedagogy-informed guidance on how to position AI as a collaborator in the construction of learning in a manner that is congruent with the values and aims of education. It offers advice based on evidence-based digital pedagogy, including frameworks for effective teaching and learning with generative AI, that enable educators and designers to put these ideas into practice.The book also explores the wider context informing this shift, connecting narratives from governments and industry leaders on the strategic importance of generative AI and lifelong learning, learning agility, and future skills that make the case for the integration of competency-based assessment into mainstream education. Finally, going beyond pedagogy and practice, it considers of some of the broader issues, including limitations and ethics, and the implications of integrating generative AI into institutional planning and design, and includes a sample roadmap for education leaders to start the process of integrating generative AI into their institutions.This work approaches the topic of generative AI from the vantage point of educators by addressing themes and concerns that will resonate with professionals in the sector.
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