SOOOO MUCH FUN! (FINALE)

Hi! This will be my final ALP post and though I have been back for quite a while now, I finally just really had time to sit down and think about the 3 months that...

SOOOO MANY DIFFERENCES (Part 2)

Hi! So even though I was very much taken aback by the differences in the first few weeks, I knew that if I was going to have a great ALP journey, I would have to...

SOOOO MANY DIFFERENCES (Part 1)

Hi! This was meant to be an earlier post but I forgot to post it after typing it out on docs so I shall modify it since my ALP experience is concluding so I would...

Week 12

Erm, ALP is over ?? WAIT, WHAT ?? ALP IS OVER !!! BLESSED This is probably one of the craziest weeks thus far. The first half of the week everyone was busy rushing through their presentation slides, going...

SOOOO MUCH FOOD (BALLER EDITION)

Hi! This will be also be a relatively short post all about food. So to repeat again from my previous post, something that I did during this trip is take pictures of everything that I...

SOOOO MUCH FOOD (BUDGET EDITION)

Hi! This will be a relatively short post but because honestly food was one of the things that I remember the most from this trip. Something that I did during this trip is take pictures...

Week 14: The Conclusion

The 3 months I have spent here in Zhejiang University has really changed me a lot. I became more appreciative of the Chinese culture, their drives, and perspectives towards the world. I have also...

Week 13: Final Presentation

We have finally arrived at this point of ALP. All our hours of hard work paid off on Friday as our presentation was met with applause and amazement. I found a new love for...

Week 12: Preparing for the End

This week was an intense week of preparation. We had a report, video, poster, and presentation due. Everyone was doing their part as a team (what a relief as the team leader). I am...

Week 11: Modeling

We have collected 50 GB worth of raw data. Finally, now comes the most exciting part of the project that we all have been waiting for – building the machine learning model. Model.train Before we start...

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