 Название: AI Revealed: Theory, Applications, Ethics
Автор: Erik Herman
Издательство: Mercury Learning and Information
Год: 2025
Страниц: 185
Язык: английский
Формат: pdf (true), epub
Размер: 14.8 MB
This book is a guide to navigating the ever-changing landscape of Artificial Intelligence. It is designed for both novices and seasoned professionals, covering a wide range of topics from fundamental concepts to cutting-edge advancements. Readers will investigate the principles of Machine Learning, explore the intricacies of Deep Learning architectures, and discover the applications of natural language processing (NLP) and computer vision. Through concise explanations and practical examples, readers will gain the knowledge and skills necessary to confidently navigate the dynamic field of Artificial Intelligence (AI). Additionally, the text examines real-world case studies and addresses important ethical issues, providing valuable insights into the ethical implications and societal impacts of technology.
Python stands as the undisputed lingua franca for AI development, owing to its readability, versatility, and the extensive ecosystem of libraries it supports. For beginners venturing into the realm of AI, mastering Python basics is paramount. This entails familiarizing oneself with Python syntax, fundamental commands, and essential data structures like lists, tuples, and dictionaries.
In the context of AI development, proficiency in key libraries is indispensable. NumPy, for instance, is instrumental for performing numerical operations and managing multidimensional arrays, a fundamental aspect of ML and data analysis. Pandas, alternately, facilitates efficient data manipulation and analysis, providing powerful tools for data preprocessing and exploration. Additionally, Matplotlib emerges as a cornerstone for data visualization, enabling developers to create insightful visual representations of their data with ease.
Python’s simplicity and elegance alleviate the learning curve for aspiring AI developers. By abstracting away complex syntax intricacies, Python empowers practitioners to focus on experimenting with AI concepts and building innovative solutions. Moreover, the supportive Python community serves as an invaluable resource for novices, offering a plethora of tutorials, documentation, and online forums for guidance and troubleshooting. This collaborative environment fosters a culture of learning and knowledge-sharing, enabling individuals to rapidly progress in their AI journey and overcome challenges encountered along the way.
This book begins with Chapter 1, offering an introduction to AI, including its definition, the various types of AI, and the core components such as machine learning, neural networks, robotics, and expert systems. We delve into the history of AI, tracing early concepts, key milestones, and the evolution of modern AI technologies.
In Chapter 2, we lay the foundations of machine learning, exploring essential concepts like supervised and unsupervised learning, and discussing model evaluation and selection methods. This chapter sets the stage for understanding how machines learn from data to make intelligent decisions.
Chapter 3 examines deep learning and neural networks, covering artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). We also touch upon advanced architectures like Generative Adversarial Networks (GANs) and Transformers, essential for understanding the cutting-edge developments in AI.
Chapter 4 introduces natural language processing (NLP), examining how AI understands and processes human language. We cover topics like text preprocessing, sentiment analysis, named entity recognition, and machine translation, showcasing AI’s capabilities in interpreting and generating human language.
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Chapter 9 serves as a practical guide for readers interested in AI development. It covers setting up a development environment, introduces Python for AI, and provides an overview of popular AI libraries, helping readers start their journey in AI programming.
Features:
Practical applications and case studies with a section on use cases across various industries, including healthcare, finance, transportation, and retail.
Actionable steps for getting started with AI include how to set up an AI development environment, learning Python for AI applications, and utilizing popular AI libraries.
Resources for further study including, AI online courses, AI communities and forums, and recommended books essentially, a roadmap for continuous learning.
Table of contents:
1: Introduction to Artificial Intelligence. 2: Foundations of Machine Learning. 3: Deep Learning and Neural Networks.
4: Natural Language Processing (NLP). 5: Computer Vision. 6: Ethics and Bias in AI 7: AI in Practice Industry Case Studies. 8: Future of AI and Emerging Technologies. 9: Getting Started with AI Development. Appendix A: Overview Of the Lisp Programming Language. Appendix B: Resources and Community. Index.
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