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For many high school graduates, college is a way to get ahead, but going to college is not the only way for young adults to succeed. Many people choose to enter the workforce after high school to start earning money and gaining experience right away. These motivated young workers can have rewarding jobs without ever having to earn a 4-year college degree. If you're interested in cars and don't know that you want to—or can—go to college, a career in car repair and maintenance might be for you. Young people need only a high school diploma or equivalent to start in car repair and maintenance—and they can eventually earn more than $50,000 a year. In Car Mechanics, you'll learn how to start a career in auto repair and what you need to succeed in the field. Find out about the prospects for these careers in the future, how much car repair workers can make each year, and whether your path to success includes a career as a car mechanic.
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Americans rely on auto mechanics to repair and maintain the vehicles they drive every day. The work of an auto mechanic has changed over the years: what was once seen as a "dirty job" is now often done on laptops in an office. Featuring profiles of real-life auto mechanics, this accessible guidebook provides readers with comprehensive information about training and apprenticeships as well as an overview of areas of specialization within the auto industry. Readers will also learn about the affect of advancements in auto technology and the future of mechanics in the era of self-driving cars.
Introduces the profession of auto mechanic, including its history, tools, training programs, and areas of specialization.
Rooted in the creative success of over 30 years of supermarket tabloid publishing, the Weekly World News has been the world's only reliable news source since 1979. The online hub www.weeklyworldnews.com is a leading entertainment news site.
AI isn’t magic. How AI Works demystifies the explosion of artificial intelligence by explaining—without a single mathematical equation—what happened, when it happened, why it happened, how it happened, and what AI is actually doing "under the hood." Artificial intelligence is everywhere—from self-driving cars, to image generation from text, to the unexpected power of language systems like ChatGPT—yet few people seem to know how it all really works. How AI Works unravels the mysteries of artificial intelligence, without the complex math and unnecessary jargon. You’ll learn: The relationship between artificial intelligence, machine learning, and deep learning The history behind AI and why the artificial intelligence revolution is happening now How decades of work in symbolic AI failed and opened the door for the emergence of neural networks What neural networks are, how they are trained, and why all the wonder of modern AI boils down to a simple, repeated unit that knows how to multiply input numbers to produce an output number. The implications of large language models, like ChatGPT and Bard, on our society—nothing will be the same again AI isn’t magic. If you’ve ever wondered how it works, what it can do, or why there’s so much hype, How AI Works will teach you everything you want to know.
Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world “friend of a friend” recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user’s interest similarity. To identify the interest similarity, a user’s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.