Vietnamese language support?

[vanloc0301]

Hi, I'm also a Vietnamese developer.

I think you should update your repository with mother tongue. All developers at Vietnam will be proud of you.

Although, your repository contains almost link reference to another resource.

But I think you can update to the Vietnamese language because your repository contains many languages like Chinese, Brazilian-Portuguese language.

Your repository was very useful. Machine learning is new almost people in Vietnam.

I hope it becomes popular.

English is not the main language, and I need to learn more. So, I tried to contribute to your repository at a free time.

Keep going and you will grow up. Thanks, @Nam Vu.

[ZuzooVn]

@vanloc0301 👍 Thanks for your comments. Currently, I have the plan to support Vietnamese Language and Japanese Language. Firstly, I will add Vietnamese version a.s.a.p. You can discuss this topic at https://daynhauhoc.com/t/lo-trinh-hoc-machine-learning-deep-learning-tu-dau-cho-cac-ban-lap-trinh-vien/37264 (Vietnamese)

Any comments and suggestions are welcome.

[vanloc0301]

That's cool.

In my opinion, I have a suggestions like: Your repository guide software engineer goes to step-by-step.

So, I found almost developer ask question like(in your topic in DNH and many topics like this:

Q: What is the best programming language for Machine Learning? A: Python or C++ is the main language. But Rust or Scala can do same. Q: What is Python? What is C++? What is Rust or Scala? A: ???

So, I think you should answer the question in your repository.

[ZuzooVn]

@vanloc0301 Thanks. I am just a newbie who wanna explore the machine learning world. Any contribution will be appreciated.

[vanloc0301]

I'm too. I love your repository and machine learning world.

[vanloc0301]

Are you read this comment from hackernews?

I made similar lists 4 years ago. I quit my job, and applied to a PhD program in ML (still doing that). Looking back, I can offer the following advice if you goal is to become a ML Engineer: Foundation: Start with (re)learning math. Take those boring university level full length courses in calculus, linear algebra, and probability/statistics. No, unless you're a fresh STEM graduate, 10 page math refreshers won't do. If you're self-studying, make sure you do all exercises and take practice exams to test yourself. Tools:

1. (Re)learn C/C++, as well as a linear algebra library, such as Numpy or MATLAB. You will also have to learn parallel and distributed programming at some point (CUDA, MPI, OpenMP, etc). Next take a boring university level full length course on algorithms and data structures.
2. Get a book describing ML algorithms, and implement them yourself, first using plain C, then with MPI or CUDA, and finally using plain Numpy/MATLAB, or one of the low-level ML frameworks (Theano or TensorFlow). Application: Finally, start doing ML. Not learning about it, doing it. Choose an application that interests you (computer vision, NLP, speech recognition, etc), and start learning what you need to make something work). Focus on specific, practical tasks. If you don't have any particular application in mind, go to Kaggle, choose a competition, and read what models/tricks the winners used. Then jump right in and start competing. The first two requirements might take years to master, but if you skip them, you won't be able to do any serious work in ML, or even understand latest papers. You will be a script kiddie, not a hacker.

It contains many pieces of advice from the experienced person.

[ZuzooVn]

I read all of comments.

I think it's suitable for machine learning research. From this HackerNews topic, I received some advice from ML Engineer at a big tech company in Silicon Valley. I am finding more good materials for this roadmap.

[vanloc0301]

@STEW1327 , your comment isn't clear. Please explain or provider more information.

[ZuzooVn]

@vanloc0301 Please ignores the spam comments.

I killed them all.

[ghost]

Cho mình hỏi về Machine learning overview và Machine learning mastery: có cần phải đọc hết các nội dung của từng bài không? vd:

• Quora có cần phải đọc hết các answers hay không?
• Trong 1 bài blog, sẽ có reference rất nhiều link, từ link đó sẽ reference tiếp nhiều link nữa => vậy có cần phải xem hết các link không?