6 Key Concepts in Andrew NG’s “Machine Learning Yearning”

6 Key Concepts in Andrew NG’s “Machine Learning Yearning”

If you are diving into AI and Machine Learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.

Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them. If you aspire to be a technical leader in AI, this book will help you on your way. Historically, the only way to learn how to make strategic decisions about AI projects was to participate in a graduate program or to gain experience working at a company. Machine Learning Yearning is there to help you quickly acquire this skill, which enables you to become better at building sophisticated AI systems.

Andrew NG is a computer scientist, executive, investor, entrepreneur, and one of the leading experts in Artificial Intelligence. He is the former Vice President and Chief Scientist of Baidu, an adjunct professor at Stanford University, the creator of one of the most popular online courses for machine learning, the co-founder of Coursera.com and a former head of Google Brain. At Baidu, he was significantly involved in expanding their AI team into several thousand people.

The book starts with a little story. Imagine, you want to build the leading cat detector system as a company. You have already built a prototype, but unfortunately, your system’s performance is not that great. Your team comes up with several ideas on how to improve the system, but you are confused about which direction to follow. You could build the worlds leading cat detector platform or waste months of your time following the wrong direction.

This book is there to tell you how you can decide and prioritize in a situation like this. According to Andrew NG, most machine learning problems will leave clues about the most promising next steps and about what you should avoid doing. He goes on explaining that learning to “read” those clues is a crucial skill in our domain.

In a nutshell, ML Yearning is about giving you a deep understanding of setting the technical direction of machine learning projects.

Since your team members could react skeptically when you propose new ideas of doing things, he made the chapters very short (1–2 pages), so that your team members could read it in a few minutes to understand the idea behind the concepts. If you are interested in reading this book, note that it is not suited for complete beginners, since it requires basic familiarity with supervised learning and deep learning.

In this post, I will share six concepts of the book in my own language out of my understanding.

NG emphasizes throughout the book that it is crucial to iterate quickly since machine learning is an iterative process. Instead of thinking about how to build the perfect ML system for your problem, you should build a simple prototype as fast as you can. This is especially true if you are not an expert in the domain of the problem since it is hard to correctly guess the most promising direction.

You should build a first prototype in just a few days and then clues will pop up that show you the most promising direction to improve the performance of the prototype. In the next iteration, you will improve the system based on one of these clues and build the next version of it. You will do this again and again.

He goes on explaining that the faster you can iterate, the more progress you will make. Other concepts of the book, build upon this principle.

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