Download or read book The Science of Artificial Intelligence Mastering the Learning Surface written by Michael Sinyangwe and published by . This book was released on 2019-01-14 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book which draws together state-of-the-art theory on data science techniques, and applies it to support the development of artificial intelligence systems which solve real world quantitative projection problems.

Download or read book The Science of Artificial Intelligence Part 2 Mastering the Qualitative Learning Surface written by Michael Sinyangwe and published by . This book was released on 2019-03-20 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a framework for state-of-the-art machine learning techniques, which have been applied to the domain of sensual learning, thereby allowing AI systems to perceive the short-, medium-, and long-term configurations of their local environments. The focus is on machine image object recognition algorithms.

Download or read book The Science of Artificial Intelligence Part 1 Mastering the Quantitative Learning Surface written by Michael Sinyangwe and published by . This book was released on 2019-03-02 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book which draws together state-of-the-art theory on data science techniques, and applies it to support the development of artificial intelligence systems which solve real world quantitative projection problems.

Download or read book The Science of Artificial Intelligence Part 5 Mastering the Probabilistic Learning Surface written by Michael Sinyangwe and published by Independently Published. This book was released on 2021-02-24 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines my theories on probabilistic machine learning. It is meant to be used in conjunction with information from my other machine learning books.

Download or read book Data Science for Beginners written by Russel R Russo and published by . This book was released on 2020-10-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you fascinated by Data Science but it seems too complicated? Do you want to learn everything about Artificial Intelligence but it looks like it is an exclusive club? If this is you, please keep reading: you are in the right place, looking at the right book. SInce you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Everyone knows (well, almost everyone) how important Data Science is for the growth and success of the biggest tech companies, and many people know about the Machine Learning impact in science, medicine and statistics. Also, it is quite commonly known that Artificial Intelligence, Machine Learning Deep Learning, and the mastering of their most important language, Python, can offer a lot of possibilities in work and business. And you yourself are probably thinking "I surely can see that opportunity, but how can I seize it?" Well, if you kept reading so far you are on the right track to answer your question. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Python and Neural Networks programming is The Skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to find your way around Artificial Intelligence, Data Science, Deep Learning and Machine Learning, and to do that Data Science for Beginners is the best way. In Data Science for Beginners you will discover: The most effective starting points when training deep neural nets The smartest way to approach Machine Learning What libraries are and which one is the best for you Tips and tricks for a smooth and painless journey into artificial intelligence Why decision tree is the way The TensorFlow parts that are going to make your coding life easy Why python is the best language for Machine Learning How to bring your ideas into a computer How to talk with deep neural networks How to deal with variables and data The most common myths about Machine Learning debunked Even If you don't know anything about programming, understanding Data Science is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Data Science is what you want to understand. Buy now Data Science for Beginners to start your path of Artificial Intelligence.

Download or read book Beyond the Surface written by Elena Sterling and published by Independently Published. This book was released on 2024-04-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) has come a long way since its inception, and deep learning and machine learning have been at the forefront of this evolution. In this book, we have explored the fundamentals of deep learning and machine learning, their applications, and the future of AI.We have seen how deep learning and machine learning have revolutionized various industries, from image and speech recognition to natural language processing and autonomous vehicles. We have also discussed the importance of data, the role of algorithms, and the need for ethical considerations in AI development. The future of AI holds immense promise, with deep learning and machine learning continuing to play a crucial role. We can expect to see advancements in areas like edge AI, autonomous systems, and explainable AI. However, we must also address the challenges and limitations of AI, including data quality, bias, and job displacement. As we move forward, it is essential to prioritize transparency, accountability, and responsibility in AI development. We must ensure that AI benefits society as a whole and does not perpetuate existing inequalities. In conclusion, AI has the potential to transform our lives and society in profound ways. By understanding the fundamentals of deep learning and machine learning, and by addressing the challenges and limitations of AI, we can create a brighter future for all. Key Points: - Deep learning and machine learning have revolutionized various industries - AI has the potential to transform our lives and society in profound ways - Prioritizing transparency, accountability, and responsibility in AI development is essential - Addressing challenges and limitations of AI, including data quality, bias, and job displacement, is crucial - Ensuring AI benefits society as a whole and does not perpetuate existing inequalities is vital By keeping these points in mind, we can harness the power of AI to create a better future for all

Download or read book Mastering Artificial Intelligence and Machine Learning written by Nikhilesh Mishra and published by Independently Published. This book was released on 2023-08-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embark on an illuminating journey through the captivating realm of "Mastering Artificial Intelligence and Machine Learning: Concepts, Techniques, and Applications" From foundational principles to cutting-edge applications, this comprehensive book equips you with the knowledge and insights to harness the transformative power of AI and ML. Uncover the core principles of AI and ML, from algorithms to predictive modeling. Dive deep into neural networks, deep learning, and natural language processing. Explore real-world applications in healthcare, finance, and more. Discover the ethical dimensions of AI's impact on society. Enhance your growth potential with an exclusive section dedicated to interviews and interviewers, providing valuable insights and skills that amplify your journey towards success. Whether you're a tech enthusiast or a seasoned professional, "Mastering Artificial Intelligence and Machine Learning: Concepts, Techniques, and Applications" empowers you to transform your understanding and become a visionary in shaping the future of technology. Don't miss out-get your copy today and embark on a journey of innovation and knowledge!

Download or read book Life of AI written by William Krystal and published by Independently Published. This book was released on 2020-06-10 with total page 812 pages. Available in PDF, EPUB and Kindle. Book excerpt: About Life of Artificial Intelligence Life of Ai Book Offers a Comprehensive Artificial Intelligence Program that will help you work on today's cutting-edge technology - Artificial Intelligence (AI). As part of this best AI teaching, you will master various aspects of Data Science (DS), Machine Learning (ML), Natural Language Processing (NLP), Deep Learning (DL), Reinforcement Learning (RL), IOT (Internet of Things), Python for Artificial Intelligence, Robotics and much more.What will you learn in this Life of Ai Book?The main goal of this book is to familiarize you with all aspects of AI so that you can start your career as an artificial intelligence engineer. A few of the many topics/modules that you will learn in the program are: -Basics of Artificial Intelligence techniques-Data Science-Machine Learning -Natural Language Processing-Deep Learning-Reinforcement Learning-Internet of Things-Python for Artificial Intelligence-Robotics

Download or read book The Sentient Machine written by Amir Husain and published by Simon and Schuster. This book was released on 2017-11-21 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explores universal questions about humanity's capacity for living and thriving in the coming age of sentient machines and AI, examining debates from opposing perspectives while discussing emerging intellectual diversity and its potential role in enabling a positive life.

Download or read book Adversarial Machine Learning written by Aneesh Sreevallabh Chivukula and published by Springer Nature. This book was released on 2023-03-06 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: A critical challenge in deep learning is the vulnerability of deep learning networks to security attacks from intelligent cyber adversaries. Even innocuous perturbations to the training data can be used to manipulate the behaviour of deep networks in unintended ways. In this book, we review the latest developments in adversarial attack technologies in computer vision; natural language processing; and cybersecurity with regard to multidimensional, textual and image data, sequence data, and temporal data. In turn, we assess the robustness properties of deep learning networks to produce a taxonomy of adversarial examples that characterises the security of learning systems using game theoretical adversarial deep learning algorithms. The state-of-the-art in adversarial perturbation-based privacy protection mechanisms is also reviewed. We propose new adversary types for game theoretical objectives in non-stationary computational learning environments. Proper quantification of the hypothesis set in the decision problems of our research leads to various functional problems, oracular problems, sampling tasks, and optimization problems. We also address the defence mechanisms currently available for deep learning models deployed in real-world environments. The learning theories used in these defence mechanisms concern data representations, feature manipulations, misclassifications costs, sensitivity landscapes, distributional robustness, and complexity classes of the adversarial deep learning algorithms and their applications. In closing, we propose future research directions in adversarial deep learning applications for resilient learning system design and review formalized learning assumptions concerning the attack surfaces and robustness characteristics of artificial intelligence applications so as to deconstruct the contemporary adversarial deep learning designs. Given its scope, the book will be of interest to Adversarial Machine Learning practitioners and Adversarial Artificial Intelligence researchers whose work involves the design and application of Adversarial Deep Learning.

Download or read book Mastering in Music written by John Paul Braddock and published by CRC Press. This book was released on 2020-12-29 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mastering in Music is a cutting-edge edited collection that offers twenty perspectives on the contexts and process of mastering. This book collects the perspectives of both academics and professionals to discuss recent developments in the field, such as mastering for VR and high resolution mastering, alongside crucial perspectives on fundamental skills, such as the business of mastering, equipment design and audio processing. Including a range of detailed case studies and interviews, Mastering in Music offers a comprehensive overview of the foremost hot topics affecting the industry, making it key reading for students and professionals engaged in music production.

Download or read book AI generated Content written by Feng Zhao and published by Springer Nature. This book was released on 2023-12-03 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the First International Conference, AIGC 2023, held in Shanghai, China, during August 25–26, 2023 The 30 full papers included in this volume were carefully reviewed and selected from 62 submissions. The volume focuses on the remarkable strides that have been made in the realm of artificial intelligence and its transformative impact on content creation. As delving into the content of the proceedings, the readers will encounter cutting-edge research findings, innovative applications, and thought-provoking insights that underscore the transformative potential of AI-generated content.

Download or read book Artificial Intelligence and Patents written by Jonathan P. Osha and published by Kluwer Law International B.V.. This book was released on 2023-09-14 with total page 567 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (‘AI’) and the AI sub-field of Machine Learning (‘ML’) are terms that originated in the fields of computer and data science but now form part of the common vernacular. AI has now found application in virtually every field. Some applications of AI have become part of our daily lives: virtual assistants, chatbots, search engines, online language translation and eCommerce all employ AI in various forms. Generative AI such as OpenAI’s products ChatGPT (natural language generation), Jukebox (music generation) and DALL-E2 (image generation) have captured the public attention to an enormous degree and can, indeed, do amazing things. A myriad of other applications of AI are found in disparate fields that, while not as visible on a daily basis, impact on our lives in a wide variety of ways. With this rapidly-increasing impact comes not only exciting new technical capabilities but also new challenges for intellectual property (‘IP’) law. Are current laws fit for purpose or is something new or different needed? This is not a new question; one need only look back to the early days of digital music, computer software and 3-D printing to find similar discussions of whether existing IP law is suited to emerging technologies. For the most part, the answer in the past has been “yes”, with perhaps a tweak here and there. Whether the same will be true of AI is, as yet, an open question. This book focuses specifically on AI and patents. Unsurprisingly, different jurisdictions have taken different approaches to patentability of AI-related inventions. Terminology (what is an “AI-related invention”?) also is inconsistent from one patent office to the next. These factors combine to create a maze of laws and regulations that patent applicants must navigate to secure protection for their innovations. To facilitate comparison of laws and practices, this book introduces a taxonomy that separates AI-related inventions into five conceptual categories. The patent law implications of each category are then addressed in national and regional chapters reflecting the perspectives of 16 major jurisdictions. All chapters follow the same structure, thereby allowing the reader to directly compare approaches taken by different jurisdictions. Thirty-nine subject matter experts from around the world contributed to this book. This is the eighth volume in the AIPPI Law Series which has been established together with the International Association for the Protection of Intellectual Property (AIPPI), a non-affiliated, non-profit organization dedicated to improving and promoting the protection of intellectual property at both national and international levels.

Download or read book Artificial Intelligence Methods in the Environmental Sciences written by Sue Ellen Haupt and published by Springer Science & Business Media. This book was released on 2008-11-28 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

Download or read book Cybersecurity and Artificial Intelligence written by Hamid Jahankhani and published by Springer Nature. This book was released on with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book AI at War written by Sam J Tangredi and published by Naval Institute Press. This book was released on 2021-03-15 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) may be the most beneficial technological development of the twenty-first century.Media hype and raised expectations for results, however, have clouded understanding of the true nature of AI—including its limitations and potential. AI at War provides a balanced and practical understanding of applying AI to national security and warfighting professionals as well as a wide array of other readers. Although the themes and findings of the chapters are relevant across the U.S. Department of Defense, to include all Services, the Joint Staff and defense agencies as well as allied and partner ministries of defense, this book is a case study of warfighting functions in the Naval Services—the U.S. Navy and U.S. Marine Corps. Sam J. Tangredi and George Galdorisi bring together over thirty experts, ranging from former DOD officials and retired flag officers to scientists and active duty junior officers. These contributors present views on a vast spectrum of subjects pertaining to the implementation of AI in modern warfare, including strategy, policy, doctrine, weapons, and ethical concerns.

Download or read book Encyclopedia of Data Science and Machine Learning written by Wang, John and published by IGI Global. This book was released on 2023-01-20 with total page 3296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.