Hướng dẫn học lập trình trí tuệ nhân tạo (AI) cho người mới bắt đầu

Trong thời gian vừa qua, tôi bắt đầu học Deep learning, hướng đi mới của trí tuệ nhận tạo (AI) trong thập kỷ vừa qua.

Với kinh nghiệm tự học và làm các projects AI ứng dụng trong ngoại ngữ cùng nền tảng nhiều năm kinh nghiệm trong ngành IT, tôi nghĩ mình có thể chia sẻ một số trải nghiệm để giúp các bạn đang có ý định bắt đầu lập trình AI có thể thấy được một lộ trình học rõ ràng hơn và qua đó tạo ra động lực để giúp bạn bắt đầu.

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A Transformer model for inserting Vietnamese accent marks

Updates:  The trained model + instructions to use can now be downloaded from HF here.

In this post, I summarize how I made use of Huggingface’s transformer library to re-solve an NLP problem related to the Vietnamese language.

The problem

After learning about Hidden Markov models about 10+ years ago, I decided to apply it to building a small, but practical, toy that can auto insert accent marks for Vietnamese language.

In a nutshell, Vietnamese has some letters that have additional marks put on them. For ex, in addition to the letter ‘a’, the Vi alphabet also contains these “marked versions”: ă, â.

And for each of these 3  versions (a, ă, â), we can then put the 5 tones on them. An example for ‘ă’ will be:  ắ (acute),  ằ (grave), ẳ (hook), ẵ (tilde), ặ (dot).

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A few notes on Items Response Theory (IRT) and Computerized adaptive testing

Recently, I was thinking about how to improve the accuracy of assessment tests for ESL learners and so I googled and found Computerized Adaptive Testing (CAT).

During the process, I accidentally discovered an interesting theory behind it. It’s called Items Response Theory or IRT for short.

So I’ve spent some time reading up about it and in the process, picked up a few very useful bits about statistical hypothesis testing, which I’m very glad to have learned.

Below, I share the most important ideas about IRT that I’ve learned.

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Interesting points from “The best way to learn Math facts”

I recently happened to come across an article on the topic of learning Math facts, titled “Fluency Without Fear: Research Evidence on the Best Ways to Learn Math Facts” by Jo Boaler.

The topic of making Math cool again is, in my opinions, very critical b/c it’s the foundation of all sciences and it should have been cool. The way it’s currently taught and approached makes it uninteresting and scary.

Below are a couple of interesting points I gathered from the above article:

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2 interesting points from Andrew Ng’s interview with Geoffrey Hinton

In this interview in a Coursera course by Andrew Ng with Geoffrey Hinton, who according to Ng is one of the “Godfathers of Deep learning”, I found 2 points that were quite interesting and thought-provoking.

On research direction

When asked about his advice for grad students doing research, Hinton said, at about 30 mins in:

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“Yes, I have”: a story about an encounter with Itzhak Perlman

This is a story about an encounter of a guest with Itzhak Perlman, as told by Matthew Kelly in his book The Rhythm of Life.

The story begins:

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