Almost Year End
18 Nov 2019I have got a feeling that, especially for my master course, no matter how hard one thing seems to be at the very beginning, once you start the progress, it no longer feels the same.
This is my very first try of blogging, so instead of making it a clear story about one particular topic, it is more like a summary of the progress.
Work - OCBC AI Lab
Last year when I was attending Nuno’s class on Negotiation, one of the projects was 10 No’s. You have to get 10 rejections for the same task, and Nuno said you would soon find out how hard it is to get repeatedly rejected. That project eventually landed me in OCBC AI Lab.
I didn’t take a break, left Wego at 31st Jul and joined OCBC 3 days later. There are pros & cons between the two, and you can never knew beforehand. Nowadays I can focus on machine learning projects, not bothering with some BI stuff like making dashboards, but I no longer have the privilege of doing things all on the cloud.
Projects in AI Lab are not linear progress. It’s more like you keep trying until find all the dead ends. Then restart again. It’s quite an experience, I guess that’s why they need to hire PhDs. They just so used to this type of back and forth progress. The key is keep trying.
These are the projects I’m currently working on:
| Course | Difficulty | Start Date | Progress | Note |
|---|---|---|---|---|
| Enterprise Chatbot | Hard |
Aug’18 | Deployment | Luis.ai & DialogFlow |
| Card Fraud | Hard |
Jul’19 | Phase 2 | Anormaly Detection |
| Cheque OCR | Hard |
Sep’19 | POC | CNN + RNN |
Study - Georgia Tech - M.S. CS
During the last days in Wego, apart from Nuno’s class, I’ve also got my GMAT score (700). It wasn’t that high but enough for me to start applying some schools. In the end, I didn’t try to apply for an MBA program but instead several part-time master courses. I chose the Master of Science in Computer Science from Georgia Institute of Technology, because other programs seem to care more about charging high fees. This program OMSCS seems to be the most affordable choice while the quality seems great from reviews.
The specialization I decide to do is Machine Learning, which is a natural choice since I’ve been doing it for years. The details of each course I would elaborate on future posts. But here is the general plan:
| Course | Difficulty | Semester |
|---|---|---|
| CS7638 Artificial Intelligence for Robotics | Hard |
19’Spring |
| CS7646 Machine Learning for Trading | Medium |
19’Summer |
| CS6476 Computer Vision | Hard |
19’Fall |
| CS6300 Software Development Process | Easy |
19’Fall |
Side tracks
I finished most of the interesting specializations on Coursera while I was with Wego. A bit too much when I’m looking back now. This year I stopped Coursera since I’m already on my master degree journey. However, I did go back to school to take CS6101 for 2 consecutive semesters. They were really difficult courses.
- First semester (18’Fall) - NLP following Stanford’s CS224N
- Second semester (19’Spring) Deep Reinforcement Learning following UC Berkeley’s CS285
- Subsequently (19’Summer), I was granted CES to take Udacity’s Deep Learning Nano Degree
Others
I’m still making time for Gym and exercise. However, since I injured my ankle, I’ve skipped most of my regular soccer and basketball games.
This year I decided to finally pick up my piano classes. Conveniently, there is a piano school 50m away from my place. Though the teacher asked me to try ABRSM grade 7, I felt too tired to make another commitment. Nothing can be enjoyable anymore if you put an exam on it.
These are the 2 songs I would like to master in next year.
- Piano Sonata No. 8 in C minor, Op. 13 III. Rondo: Allegro
- Prelude and Fugue in C minor, BWV 847
And by the very end of next year, hopefully, I can start this two:
- Chopin: Fantaisie-impromptu in C-Sharp Minor, Op. 66
- Beethoven: The Piano Sonata No. 14 in C♯ minor “Quasi una fantasia”, Op. 27, No. 2
I believe now and then you should stop and think about long term goals. My next year top priority would still be work and study. But apart from that, instead of more sidetrack studies, I would like to try to get some Kaggle exposure and tech sharing. Use Medium to replicate SOTA results would be a good start, eventually, I’d also like to try a publication. May need some help from Prof on this part.
Data Science is an exhausted journey, I believe the best way through is to keep it fun.
Satisfaction = Perception - Expectation
Travel
I need to list it separately since I’ve been spending too much on this category.
Travel always play a big part of my life. I believe that it is such a beautiful world, one need to see it through their own eyes.
| Time | Places | Sub-places | Score |
|---|---|---|---|
| Dec’18 | China | Beijing, Pingyao, Xi’An | ⭐⭐ |
| Jan’19 | China | Dalian | ⭐⭐⭐⭐⭐ |
| May’19 | Japan | Kobe, Himeji, Kyoto, Osaka | ⭐⭐⭐⭐ |
| Aug’19 | Central Europe | Germany, Czech, Slovenia, Croatia, Montenegro, Hungary, Slovakia | ⭐⭐⭐⭐⭐ |
| Oct’19 | Myanmar | Yangon, Bagan | ⭐⭐⭐⭐ |
| Dec’19 | China, Korea | Shenzhen, Chengdu, Chongqing, Ningbo, Seoul, Dalian | ⭐⭐⭐ |