BOOKS - Harnessing Python for Generative AI
Harnessing Python for Generative AI - Anand Vemula 2024 PDF | AZW3 | EPUB | MOBI Independently published BOOKS
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Harnessing Python for Generative AI
Author: Anand Vemula
Year: 2024
Format: PDF | AZW3 | EPUB | MOBI
File size: 10.1 MB
Language: ENG



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The book covers the basics of generative AI, including the history and current state of the field, as well as practical tutorials on how to use Python libraries such as TensorFlow and Keras to build and train generative models. The book begins by exploring the concept of generative AI and its potential applications in various industries, from art and design to healthcare and finance. It then delves into the technical aspects of building and training generative models, including the different types of algorithms and their strengths and weaknesses. Finally, it provides practical examples of how to implement these models in real-world scenarios. The book also discusses the challenges and limitations of generative AI, such as the risk of bias and ethical considerations, and offers guidance on how to address these issues in a responsible and ethical manner. Throughout the book, the author emphasizes the importance of understanding the process of technological evolution and developing a personal paradigm for perceiving the technological process of developing modern knowledge as the basis for the survival of humanity and the survival of the unification of people in a warring state. The book is written in an accessible and engaging style, making it suitable for both beginners and experienced programmers and data scientists who want to learn about generative AI and its applications.
Книга охватывает основы генеративного ИИ, включая историю и текущее состояние области, а также практические руководства по использованию библиотек Python, таких как TensorFlow и Keras, для построения и обучения генеративных моделей. Книга начинается с изучения концепции генеративного ИИ и его потенциальных применений в различных отраслях, от искусства и дизайна до здравоохранения и финансов. Затем он углубляется в технические аспекты построения и обучения генеративных моделей, включая различные типы алгоритмов и их сильные и слабые стороны. Наконец, приводятся практические примеры реализации этих моделей в реальных сценариях. В книге также обсуждаются проблемы и ограничения генеративного ИИ, такие как риск предвзятости и этические соображения, и предлагаются рекомендации о том, как ответственно и этично решать эти проблемы. На протяжении всей книги автор подчёркивает важность понимания процесса технологической эволюции и выработки личностной парадигмы восприятия технологического процесса развития современного знания как основы выживания человечества и выживания объединения людей в воюющем государстве. Книга написана в доступном и увлекательном стиле, что делает ее подходящей как для начинающих, так и для опытных программистов и специалистов по анализу данных, которые хотят узнать о генеративном ИИ и его приложениях.
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