After studying further about what neural networks can do, I realized this is what people are going to do in the future. For everything. And I know I got to get a piece of it. So I started to learn the basics. At the time there was not much training or tutorials. AlexNet was widely used and I ran across a tutorial on using Matlab to make VGG16 convolutional NN. I had pretty good video card, but it wasn’t able to run the Matlab package. I had my eyes on the upcoming Nvidia 1070 cards. I had to wait for it to release and I new it was going to be hard to get.
In the mean time, I figured out it doesn’t have to run GPU. And I can start learning the basics before I can get my hands on the new cards. I searched around. Most of the tutorial have fragmented information, and I feel I have no grasp on the material and did not really understand anything. Then I found Stanford’s CS213n class notes, and felt I hit the gold mine. It was technical and theoretically involved, but it explained how CNN model pixels and many of the CNN tools like weight initialization and dropout.
Once I got a sense of how CNN works, I joined Kaggle’s competitions to actually ran my own models. I tried the simple ones like Titanic morality prediction that require heavy feature engineering, but quickly realized that’s not the way to do things, because it required too much manual work and introduced bias. Then I saw the competition where State Farm want to categorize pictures taken inside of cards for what drivers were doing. That required me to use opencv for image pre-processing and categorized pictures using training and test sets. I didn’t end up having a super high ranking, but I learned various ways of doing it from people’s published kernels.
Fast forward several month, and I got my hand on a Nvidia 1070. Popped that puppy in the tower and sped up the models by a huge chunk. Of course, by this point, I knew Python is the way to go and used Theano and Tensorflow for making the models. I played with some public data and started a project to estimate house price in NYC, because everybody was doing it. I tried out for an interview with Insight Data Science. I prepared my projects and seemed to know what I talked about. I was told that I was on the board line and barely missed the cut, but maybe she was just being nice.
No problem, I will try again. On the third real try for Data Incubator, I finally answered enough questions in Python and got an interview. After a group interview with 6 other applicant, I got an offer for the paid, second tier fellowship program. Supposedly, I wasn’t as good as what they are looking, but if I pay for the fellowship, I can tag along. It was a large amount of money, even for NYC standards, but I had other considerations.
My Professor was about ran out of funding, at least for me. So I was about to be out of a job, unless I want to find another postdoc position. I started looking for industry jobs, but wasn’t successful. Just one week earlier, I got an offer to join a doctor’s office to do In Vitro Fertilization stuff. I applied because I have many family connections in that field, and I actually know how to do the stuff. It was a safe job with limited upside. My wife encouraged me to turn it down and pursuit my dream in AI. And I thank her so much now. I would be doing the same thing day after day to earn a living if I took that job. But instead, I’m learning new things everyday and loving it.
Coming back to my decisions, it was tough to turn down offers when I about to be unemployed. Again, my wife agreed to “take care of me” for 6 month, since my research said that’s the average time of unemployment for changing careers. It gave me a piece of mind, and let me focus on what to learn. With all my personal affaires lined up, I accepted the layoff and prepared to be workless, payed a hefty amount for the fellowship, and doubled downed on my future.
The fellowship was enjoyable. Hard, but fun. We were squeezed in a box for at least 8 hours a day. Studied as much as we could, did a lot of programming, prepared for interviews, and met some potential employers. It was a good variety of topics, and I was still talking about it 6 months later. As fun as it was, time flies. But the end of the session, 2 people got offers. Some are hopeful, I wasn’t one of them.
When the time of workless came, strangely, I wasn’t afraid. Moved out of NYC, we ranted an apartment in a suburban area. I kept the discipline of applying jobs everyday and interviews kept happening. A few showed interest, but ultimately, none of them went through.
Finally, a family member helped me to find a job, but it a rollercoaster. I got a phone interview a year earlier. A statistician asked about what models I made before and some other technical questions for half an hour. No feedback or anything after that. Seven months later, I got an email about scheduling onsite interview out of the blue. Of course I went. It was a grueling 8 hour day interview. Mostly behavior questions, with a few stat questions throwing in between. Then nothing. Mostly mentioning there is no position opening right now.
A month later, I got an email from a different department asking to schedule a phone interview. This time more questions on neural networks and programming. I knew I wanted whatever job they have. But again, no feedback on job or no job. A month later, same group of people called again and simply asked do I want the job. They knew I wasn’t sold on programming, but they are willing to teach. I had to go for an onsite, but from what I felt, it was just a routine I had to go through. The decision was already made.
Everything was what I though it would be and better. The rest is history. Now I’m working on applying the latest AI algorithms to industry related tasks. Some people asked, do you feel overwhelmed by doing more than 20 projects in one year. I responded to that: I was never too busy to do the things I love.
So, for people who are looking to change their career to AI, or any career you love, don’t give up. The road may be long and scary, but we only live once, and you should go for broke.