吴恩达新书《How to build a career in AI》书摘

Three key steps of career growth are learning foundational skills, working on projects and finding a job. As you go through each step, you should also build a supportive community. Having friends and allies who can help you - and who you strive to help - make the path easier.

Chapter 2 Learning Technical Skills for a Promising AI Career

• Foundational machine learning skills - it's even more important to understand the core concepts behind how and why machine learning works.
• Math relevant to machine learning - In addition, exploratory data analysis(EDA), using visualizations and other methods to systematically explore a dataset, is an underrated stkll.
• Software development - your job opportunities will increase if you can also write good software to inplement complex AI systems.
• Knowledge in an application area

A good course is often the most time-efficient way to master a meaningful body of knowledge. When you've absorted the knowledge available in courses, you can switch over to research papers and other resources.

The best way to build a new habit is to start small and succeed, rather than start too big and fail.

Chapter 3 Should You learn Math to Get a Job in AI?

Chapter 4 Scoping Successful AI Projects

1. Find a domain expert and identify a bussiness problem(not an AI problem),
2. Brainstorm AI solutions. Once you understand a problem, you can brainstorm potential solutions more efficiently. Sometimes there isn't a good AI solution, and that's okay too.
3. Assess the feasibility and value of potential solutions.
4. Determine the metrics to aim for. ML teams are often most comfortable with metrics that a learning algorithm can optimize. But we need to strech outside our comfort zone to come up with business metrics, such as those related to user engagement, revenue and so on. If you aren't able to determine reasonable milestones, it may be a sign that you need to learn more about the problem. A quick proof of the concept can help supply the missing perspective.
5. Budget for resources.

Working on projects is an iterative progress. If, at any step, you find that the current direction is infeasible, return to an earlier step and proceed with your new understanding.

Chapter 5 Finding Projects that Complement Your Career Goals

Generate your project idea

1. Join existing projects.

2. Keep reading and talking to people.

3. Focus on an application area - do unique work among the variety application to which ML has not yet applied to.

4. Develop a side hustle.

Choose one idea to jump into

1. Will the project help you grow technically?

2. Do you have good teammates to work with?

3. Can it be a stepping stone?

Chapter 6 Building a Portfolio of Projects that Shows Skill Progressin

Scope and Complexity

1. Class projects

2. Personal projects - Kaggle et al.

3. Creating value - gain access to more equipment, compute time, labeling budget, or head count.

4. Rising scope and complexity

Note that -

• Don't worry about starting too small.

• Communication is key - You need to be able to explain your thinking if you want others to see the value in your work and trust you with resources that you can invest in larger projects.

• Leadership isn't just for managers.

• Building a portfolio of projects, especially that shows progress over time from simple to complex undertakings, will be a big help when it comes to looking for a job.

Chapter 7 A Simle Framework for Starting Your AI Job Search

• Are you switching roles?

• Are you switch industries?

Chapter 8 Using Informational Interviews to Find the Right Job

An informational interview involves finding someone in a company or role you'd like to know more about and informally interviewing them about their work.

The importance of your network and community.

Chapter 9 Finding the Right AI Job for You

• Pay attention to the fundamentals. - A compelling resume, portfolio of technical projects, and a strong interview performance will unlock doors.

• Proceed respectfully and responsibly - If you're leaving a job, exit gracefully.

• Choose who to work with

• Get help from your community

• Instead of viewing it as a great leap, consider an incremental approach. Start by identifying possible roles and conducting a handful of informational inverviews.

Chapter 10 Keys to Building a Career in AI

1. Teamwork
2. Networking - instead of trying to build up my personal network, I focus instead on building up the communities that I'm part of.
3. Job search - just one small step in the long journey of a career
4. Personal discipline
5. Altruism

Chapter 11 Overcoming Imposter Syndrome

It is easy to forget that to become good at anything, the first step is to suck at it. If you've succeeded at sucking at AI - Congrats, you're on your way.

No matter how far along you are - if you're at least as knowledgeable as a 3-year-old - you can encourage and lift up others behind you. Doing so will help you, too, as others behind you will recognize your expertise and also encourage you to keep developing.

Final thoughts

Make Every Day Count