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Nvidia just reached a trillion-dollar market cap recently because of its investment in GPU chips which is the foundation of AI magics.
Its co-founder, Jensen Huang said in his recent Commencement Speech at National Taiwan University that “We’re at the beginning of a new era”.
I feel excited.
How do I seize this opportunity which only happens a few times in my life?
I decided to study this field and learn what I can build from it. These are helpful resources.
It helps you to learn how to drive a car first instead of how to build an engine. Things attract me is that it teaches you how to deploy your Machine Learning project to a web app. So I can show my work to the world as a startup product.
Previously, I studied an online course from Coursera about Deep Learning. But I found I quickly lost motivation because I only know theories. I don’t even know how to run the code on my own laptop.
As Paul Grahma’s book <Hackers and Painters> mentioned:
“If you find yourself in the computer science department, there is a natural temptation to believe, for example, that hacking is the applied version of what theoretical computer science is the theory of. All the time I was in graduate school I had an uncomfortable feeling in the back of my mind that I ought to know more theory, and that it was very remiss of me to have forgotten all that stuff within three weeks of the final exam. Now I realize I was mistaken. Hackers need to understand the theory of computation about as much as painters need to understand paint chemistry.”
I got this feeling of lacking theoretical knowledge all the time. This feeling shouldn’t prevent us from make something interesting. After all years of school and PhD research, I start to realise that my learning process is the wrong way. I have full of theories in my head but don’t know how to implement them in real life at all. Learning should be an exciting and interesting thing. We should be able to show our knowledge and bring meaning to this world. Meantime, helps us get a decent life.
That’s why I decided to start by building something first this time. Because I don’t want to repeat the old ways and get lost in nowhere. And in the end, feel ashamed again about my poor discipline.
Although that has been said, I do highly recommend the legendary Machine Learning course taught by Andrew Ng. It helped me understand the foundation of AI. Brought me the confidence to further study.
It has many open-source projects I can learn from. I much appreciate these unknown heroes for giving away their code for free. They provided shoulders for beginners and dreamers like me to stand on. I will return to this community for sure.
3. Start a blog to show your work.
I decided to write this, my very first blog, because I was inspired by reading Why you (yes, you) should blog written by Rachel Thomas, co-founder of fast.ai and <Show Your Work!> written by Austin Kleon. My favourite quote from this book is “Show your work, not your dog”. Blog helps to build my online credential on AI and startup. If people can track what you do online, they will trust you more in what you’re doing.
Wish whoever reading this blog all the best in your AI adventure!
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