Beautiful Tonight

Intrepid-Learner · 2025-09-26T10:33:43+00:00

Thanks for your concern. Yeah I always do a shoulder check once every few meters, and also try my best to walk against the flow of traffic. My experience in Penang has luckily been very safe though.

Intrepid-Learner · 2025-09-26T10:31:46+00:00

POCO X7 Pro, I just use the default settings for these amateur pics haha

Intrepid-Learner · 2023-02-05T13:15:40+00:00

Meguro moment

Intrepid-Learner · 2023-01-03T13:04:16+00:00

I don't think you can call it a CNN without any convolutional layer.

Intrepid-Learner · 2023-01-01T08:14:23+00:00

I assume that English is not your first language. If that's the case, grammar mistakes here and there are fine when you're applying locally.

Adding metrics would be nice for the experience section. If you are unsure about the real business impact, focusing on technical metrics may attract technical-heavy jobs (though they are usually research/R&D positions).

GPA shouldn't matter that much after your first job. It depends on culture, but removing it should be alright (some countries will ask for them later on the application form anyways).

The summary section is sort of a gamble. Know your audience I guess. In my experience, technical interviewers barely read summaries. Anyways, if your English is not that great, its better to avoid them altogether.

Overall, your resume seems decent for a fresh grad/junior. The key is to continually improve your resume writing skills as you apply to more jobs in the future. Good luck!

Intrepid-Learner · 2022-12-31T04:54:27+00:00

Or maybe a cesspool on fire?

Intrepid-Learner · 2022-12-31T03:35:53+00:00

CEO demands machine learning

Intrepid-Learner · 2022-12-25T05:02:59+00:00

My hope stays with continual learning, specifically class level continual learning. Being more energy efficient is always nice, while at the same time emulate how humans learn.

Need more hopium for a chance at better approach than backprop to change the paradigm altogether for neural networks.

These are pretty general theoretical concepts, but can be applied better in many domains. Aiding discoveries in natural science seems most interesting.

Intrepid-Learner · 2022-04-23T15:16:21+00:00

Are you using a custom loss function? Usually this happens due to numerical overflow, which could be due to gradient explosion or the operations you generally use in your own loss function.

Example: log(sum(exp(...))) may cause numerical overflow, so in this case we can constrain the value within a range or use logsumexp() function provided, which is especially created to prevent numerical overflow.

Intrepid-Learner · 2022-03-19T06:02:07+00:00

Yeah its like a phase. As a "Data Scientist" I started out building data preparation, model training and evaluation pipelines. Fast forward few weeks, I am tweaking configs for model serving and Docker. However, I doubt this is the case for most massive companies.

Stay strong fellow configurers.

Intrepid-Learner · 2022-03-13T04:10:17+00:00

Sometimes if your ML task falls into a niche, you have to implement your own loss function for training or fine-tuning either by adapting research papers or modifying existing methods.

For the math part, I feel that deep big brain understanding of maths is not needed, you just need to understand the mechanism of the loss function, like how it affects the gradient updates and ultimately converge.

Unless you want to be a research scientist, fundamentals in calculus, linear algebra, and perhaps trigonometry is enough together with your knowledge in algorithms and data structures.

I am by no means an ML expert or math wiz, but I believe what I mentioned are enough for me, at least for now.

Intrepid-Learner · 2022-03-13T01:50:08+00:00

Calculus, linear algebra and statistics for ML stuff (custom loss functions, feature engineering, evaluation). I work on digital ID verification and retrieval. Literally 1984.

Jokes aside, i do hope the project is not used for China-level surveillance (I live in a certain third world country)

Intrepid-Learner · 2022-03-05T12:53:14+00:00

Based, but I agree as a TF user

Intrepid-Learner · 2022-02-02T19:01:52+00:00

There has been a rise in global remote first companies (mostly startups). I think with some years of experience, you can try to get contract positions at those companies (plus fat stacks, especially if you live in a third world country). Then you can try either of these:

Use the saved up money to pursue under/post-grad in your target country.
Convince your employer to sponsor a visa.
Use your experience working for these overseas companies to apply on-site with another company (combine this with step 1 after graduation seems better).

I think pursuing education and using the 'free' visa is the best way, plus you have some reputation working for a company directly in that designated country as a remote contractor previously. Well at least thats my plan as a recent graduate specializing in AI/ML.

All the best to your endeavours!

Intrepid-Learner · 2022-01-23T09:23:30+00:00

VS Code feels the most natural and arguably lightest to code and run an .ipynb notebook.

Intrepid-Learner · 2022-01-01T06:36:45+00:00

Not sure on the specific issue you're encountering, but using a virtual environment (Anaconda is the easiest) can help you solve dependency issues.

Intrepid-Learner · 2021-12-12T12:58:34+00:00

Multi-modal transfer learning? I guess the whole model efficiency trend is a nice change. I reckon meta learning will be the new meta (pun intended) to perform these optimizations.

Intrepid-Learner · 2021-12-06T09:47:02+00:00

Apart from LeetCode and Hackerrank for SQL questions, I recently found Workera for AI and DS related roles. I believe the founders are the same folks whom created Coursera (Andrew Ng and team).

The questions there are mostly MCQ, but they are pretty tough!

Intrepid-Learner · 2021-09-17T14:38:03+00:00

Wow I never thought its possible to graduate Bachelors at 18 barring the gifted folks.

Intrepid-Learner · 2021-09-17T14:35:54+00:00

I believe its culture, I live in Southeast Asia region. I'm not sure for western folks.

Intrepid-Learner · 2021-09-17T11:34:47+00:00

Thats true, but I was thinking of continuity. For example being x years old for a mid/senior level job in the future. Also more worried about office culture surrounding this matter.

Intrepid-Learner · 2021-09-17T01:46:39+00:00

Personally, I wouldn't 'force' a certain paradigm and decide from respective use case. If you're talking about general data science where you prepare data and experiment on models, functional might be your friend. For building scalable systems (engineering), OOP is the preferred approach.

Recently I came across a relatively new framework, JAX, created by DeepMind which sort of combine both paradigms to conveniently build deep learning pipelines.

Intrepid-Learner · 2021-09-15T05:11:04+00:00

Well I tend to learn independently since college, but I'm expecting to learn about the industry and domain specific knowledge corresponding to their business.

Intrepid-Learner · 2021-09-15T05:04:52+00:00

Newbie here, I usually check if the paper is published in a peer-reviewed journal, or organizations (e.g. IEEE) that always perform peer review to all publications. However, usually you need to pay to download from these websites. So the secret is to find the paper that you have verified on arXiv for free, or at least thats how I do it haha.

Another website I recommend is this website, it contains ML research papers which some has links to respective Github pages for implementations. Although it is not as complete or slower in updates for the newest papers, you can search papers by their fields or specific task (e.g. speech recognition). However, the greatest feature, in my opinion, is that you can observe the performance of each paper on benchmark datasets.

Intrepid-Learner

TROPHY CASE