Ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago, ten years ago. The final part of the book is available for free on the website, which is a delight for machine learning enthusiasts.
AndrewNg is well-known in the field of machine learning. Almost all machine learning professionals have read AndrewNg’s series of machine learning courses. Roughly speaking, ng’s previous machine learning courses include two: One is Machine Learning on Cousera and the other is Stanford University (CS229: Machine Learning).
On this day, Ng has also written Deep Learning video tutorial Deep LearningSpecialization and is currently working on a book called Machine Learning Ten years ago on how to organize an AI project.
This is a book Ng is working on and will continue to work on in the coming months. If you want to get a free draft of each chapter, you can sign up and get it for free:
http://www.mlyearning.org/
On the book, Ng writes:
Artificial intelligence, machine learning and deep learning are transforming many industries. My goal in writing this book is to teach you how to construct machine learning projects.
The point of this book is not to teach ML algorithms, but to make ML algorithms work. Some AI technologies give you a hammer; This book teaches you how to use a hammer. If you aspire to be a technical leader in AI and want to set the direction for your team, this book will help.
This book allows you to master the following:
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Consider the most promising directions for ai projects.
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Diagnosing errors in machine learning systems.
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Build ML in complex setups, such as mismatched training/test sets.
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Set up an ML project to compare and/or exceed human-level performance.
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Learn when and how to apply end-to-end learning, transfer learning, and multitasking learning.
Historically, the only way to learn how to make these “strategic” decisions is to spend years studying at a research institution or company. The book I’m writing will hopefully help you acquire this skill quickly so that you can build ai systems better. The book is about 100 pages long.
This book is in writing, and below is the table of contents for version 0.5
dataGet the way
Follow the public account [Pegasus Club]
Navigation recovery number [22]
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