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  • Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI

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Machine Learning Q and AI: 30 Essential Questions and Answers on Machine Learning and AI

4.6 out of 5 stars (39)

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Learn the answers to 30 cutting-edge questions in machine learning and AI and level up your expertise in the field.

If you’re ready to venture beyond introductory concepts and dig deeper into machine learning, deep learning, and AI, the question-and-answer format of
Machine Learning Q and AI will make things fast and easy for you, without a lot of mucking about.

Born out of questions often fielded by author Sebastian Raschka, the direct, no-nonsense approach of this book makes advanced topics more accessible and genuinely engaging. Each brief, self-contained chapter journeys through a fundamental question in AI, unraveling it with clear explanations, diagrams, and hands-on exercises.

WHAT'S INSIDE:

FOCUSED CHAPTERS: Key questions in AI are answered concisely, and complex ideas are broken down into easily digestible parts.

WIDE RANGE OF TOPICS: Raschka covers topics ranging from neural network architectures and model evaluation to computer vision and natural language processing.

PRACTICAL APPLICATIONS: Learn techniques for enhancing model performance, fine-tuning large models, and more.

You’ll also explore how to:
• Manage the various sources of randomness in neural network training
• Differentiate between encoder and decoder architectures in large language models
• Reduce overfitting through data and model modifications
• Construct confidence intervals for classifiers and optimize models with limited labeled data
• Choose between different multi-GPU training paradigms and different types of generative AI models
• Understand performance metrics for natural language processing
• Make sense of the inductive biases in vision transformers

If you’ve been on the hunt for the perfect resource to elevate your understanding of machine learning,
Machine Learning Q and AI will make it easy for you to painlessly advance your knowledge beyond the basics.

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From the Publisher

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Review of Machine Learning Q and AI on black background
Review of Machine Learning Q and AI on black background
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About the Author

Sebastian Raschka, PhD, is a machine learning and AI researcher with a passion for education. As Lead AI Educator at Lightning AI, he is excited about making AI and deep learning more accessible. Raschka previously was Assistant Professor of Statistics at the University of Wisconsin-Madison, where he specialized in researching deep learning and machine learning, and is the author of the bestselling books Python Machine Learning and Machine Learning with PyTorch and Scikit-Learn.

No Starch Press logo. A black circle with a white iron and a star in the center.

About the Publisher

No Starch Press has published the finest in geek entertainment since 1994, creating both timely and timeless titles like Python Crash Course, Python for Kids, How Linux Works, and Hacking: The Art of Exploitation. An independent, San Francisco-based publishing company, No Starch Press focuses on a curated list of well-crafted books that make a difference. They publish on many topics, including computer programming, cybersecurity, operating systems, and LEGO. The titles have personality, the authors are passionate experts, and all the content goes through extensive editorial and technical reviews. Long known for its fun, fearless approach to technology, No Starch Press has earned wide support from STEM enthusiasts worldwide.

Editorial Reviews

Review

“Sebastian has a gift for distilling complex, AI-related topics into practical takeaways that can be understood by anyone. His new book, Machine Learning and AI Beyond the Basics, is another great resource for AI practitioners of any level.”
–Cameron R. Wolfe, Writer of Deep (Learning) Focus

“Sebastian uniquely combines academic depth, engineering agility, and the ability to demystify complex ideas. He can go deep into any theoretical topics, experiment to validate new ideas, then explain them all to you in simple words. If you’re starting your journey into machine learning, Sebastian is your guide.”
–Chip Huyen, Author of Designing Machine Learning Systems

“Sebastian Raschka's new book,
Machine Learning Q and AI, is a one-stop shop for overviews of crucial AI topics beyond the core covered in most introductory courses...If you have already stepped into the world of AI via deep neural networks, then this book will give you what you need to locate and understand the next level.”
–Ronald T. Kneusel, author of How AI Works

About the Author

Sebastian Raschka, PhD, is a machine learning and AI researcher with a passion for education. As Lead AI Educator at Lightning AI, he is excited about making AI and deep learning more accessible. Raschka previously was Assistant Professor of Statistics at the University of Wisconsin-Madison, where he specialized in researching deep learning and machine learning, and is the author of the bestselling books Python Machine Learning and Machine Learning with PyTorch and Scikit-Learn. You can find out more about his research on his website at https://sebastianraschka.com.

Product details

  • Publisher ‏ : ‎ No Starch Press
  • Publication date ‏ : ‎ April 16, 2024
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 264 pages
  • ISBN-10 ‏ : ‎ 1718503768
  • ISBN-13 ‏ : ‎ 978-1718503762
  • Item Weight ‏ : ‎ 1.1 pounds
  • Dimensions ‏ : ‎ 7.06 x 0.58 x 9.25 inches
  • Best Sellers Rank: #790,878 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.6 out of 5 stars (39)

About the author

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Sebastian Raschka
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Sebastian Raschka, PhD is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work bridges academia and industry, including roles as senior engineering staff at an AI company and a statistics professor.

As an independent researcher and industry expert, Sebastian collaborates with companies on AI solutions and serves on the Open Source Advisory Board at University of Wisconsin–Madison.

Sebastian specializes in LLMs and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.