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

ISBN-13: 978-1718503762, ISBN-10: 1718503768

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Book details

ISBN-10

1718503768
ISBN-13

978-1718503762
Publisher

No Starch Press
Publication date

April 16, 2024
Language

English
Dimensions

7.06 x 0.58 x 9.25 inches
Print length

264 pages

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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.

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.

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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.

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Product information

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	#46,306 in Books (See Top 100 in Books) #25 in Natural Language Processing (Books) #27 in Probability & Statistics (Books) #64 in Computer Science (Books)
Customer Reviews	4.7 out of 5 stars 27Reviews

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Aaron
Verified Purchase
Great book for intermediate level to go deeper - just as it describes.
Reviewed in the United States on August 27, 2024
Format: Paperback

I found this to be a very well-written, accessible, helpful tool in going deeper in understanding AI and the concepts around it. Even beginners can get plenty out of it, although it's primarily geared toward those with a solid foundation already as others have pointed...

I found this to be a very well-written, accessible, helpful tool in going deeper in understanding AI and the concepts around it. Even beginners can get plenty out of it, although it's primarily geared toward those with a solid foundation already as others have pointed out.

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apincsak
Verified Purchase
Great Book with Good Variety of Topics
Reviewed in the United States on May 28, 2024
Format: Paperback

I thoroughly enjoyed Raschka’s book. It explored a variety of topics on machine learning and deep learning. Some of it was familiar but more than half of it was new knowledge. It expanded my knowledge on the cutting edge methods. Also I liked that all chapters had...

I thoroughly enjoyed Raschka’s book. It explored a variety of topics on machine learning and deep learning. Some of it was familiar but more than half of it was new knowledge. It expanded my knowledge on the cutting edge methods. Also I liked that all chapters had exercises. If you’re looking for something like this, I highly recommend checking it out.

One person found this helpful

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Jean-Paul Van Belle
An absolute gem: great fast-track bite-sized topics on AI - get it!
Reviewed in the United States on May 17, 2024
Format: Paperback

Being an avid reader of Raschka's excellent Ahead-of-AI newsletter, I was thrilled to find his physical book on the shelf. It's quite pricey, so I really checked out the content before buying but what a gem! Each little "Question" chapter is short, to the...

Being an avid reader of Raschka's excellent Ahead-of-AI newsletter, I was thrilled to find his physical book on the shelf. It's quite pricey, so I really checked out the content before buying but what a gem! Each little "Question" chapter is short, to the point, eminently readable and really clarifies the underlying concepts clearly. So glad I bought it. If you're navigating the space of ML & AI and wanting to understand the technical details but you don't have a strong computer science background, then this is an ideal resource and must-have! And you don't have to read the chapters in order - most of them can be read on their own.

3 people found this helpful

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Soroush
Verified Purchase
Not an introductory book to AI
Reviewed in the United States on June 3, 2024
Format: Paperback

I am a software developer and was excited to find out more about AI programming. This book clearly assumes a very high level of existing understanding of math and core AI concepts. Literarily the first concept gets into "high-dimensional" data and...

I am a software developer and was excited to find out more about AI programming. This book clearly assumes a very high level of existing understanding of math and core AI concepts. Literarily the first concept gets into "high-dimensional" data and "low-dimensional" vectors. The title is misleading because for a novice an essential question would be "what are low-dimensional" vectors, what is convolution, etc.

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