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Probably Approximately Correct: Nature'S Algorithms For Learning And Prospering In A Complex World,New
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From A Leading Computer Scientist, A Unifying Theory That Will Revolutionize Our Understanding Of How Life Evolves And Learns.How Does Life Prosper In A Complex And Erratic World? While We Know That Nature Follows Patterns Such As The Law Of Gravity Our Everyday Lives Are Beyond What Known Science Can Predict. We Nevertheless Muddle Through Even In The Absence Of Theories Of How To Act. But How Do We Do It?In Probably Approximately Correct, Computer Scientist Leslie Valiant Presents A Masterful Synthesis Of Learning And Evolution To Show How Both Individually And Collectively We Not Only Survive, But Prosper In A World As Complex As Our Own. The Key Is 'Probably Approximately Correct' Algorithms, A Concept Valiant Developed To Explain How Effective Behavior Can Be Learned. The Model Shows That Pragmatically Coping With A Problem Can Provide A Satisfactory Solution In The Absence Of Any Theory Of The Problem. After All, Finding A Mate Does Not Require A Theory Of Mating. Valiant'S Theory Reveals The Shared Computational Nature Of Evolution And Learning, And Sheds Light On Perennial Questions Such As Nature Versus Nurture And The Limits Of Artificial Intelligence.Offering A Powerful And Elegant Model That Encompasses Life'S Complexity, Probably Approximately Correct Has Profound Implications For How We Think About Behavior, Cognition, Biological Evolution, And The Possibilities And Limits Of Human And Machine Intelligence.
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- Q: What is the page count of the book? A: The book has two hundred eight pages. This length provides an in-depth exploration of its themes and concepts.
- Q: What are the dimensions of the book? A: The book measures six and a half inches in length, nine and a half inches in height, and is zero point eight eight inches thick. These dimensions make it a convenient size for reading.
- Q: What type of binding does the book have? A: The book is bound in hardcover. This type of binding ensures durability and a premium feel.
- Q: Who is the author of this book? A: The author is Leslie Valiant. He is a renowned computer scientist known for his contributions to learning theory.
- Q: What genre does this book belong to? A: The book falls under the genre of Intelligence and Semantics. It tackles complex themes related to learning and evolution.
- Q: Is this book suitable for beginners? A: Yes, the book is suitable for beginners. It presents complex ideas in an accessible manner, making it readable for a wide audience.
- Q: What is the main theme of the book? A: The main theme revolves around learning and evolution. It synthesizes these concepts to explain how life thrives in complexity.
- Q: Can this book be used as a textbook? A: Yes, it can be used as a textbook. Its insights are valuable for students studying computer science and related fields.
- Q: How should I store this book? A: Store the book upright on a shelf to maintain its shape. Avoid exposure to direct sunlight to prevent fading.
- Q: How do I care for the book? A: Keep the book clean by dusting it regularly. Avoid getting it wet to preserve its condition.
- Q: Is this book safe for young readers? A: Yes, it is safe for young readers, although it is best suited for those with an interest in science and technology.
- Q: What if the book arrives damaged? A: If the book arrives damaged, contact customer support for a return or exchange. They will assist you with the process.
- Q: Can I return the book if I don't like it? A: Yes, you can return the book if you are unsatisfied. Check the return policy for specific conditions.
- Q: What should I do if I have issues with the book? A: If you encounter issues, reach out to customer support. They can provide guidance on resolving any problems.
- Q: Does this book contain any illustrations? A: No, this book does not contain illustrations. It focuses on text to convey its complex ideas.
- Q: Is this book recommended for professionals in the field? A: Yes, it is recommended for professionals. The book offers insights that deepen understanding of learning algorithms.