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Research from the Lab's Little AI Scholars: Learn the life language using artificial intelligence

on February 06, 2024

Education NLP Research

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Lab's weekly topic - week 50 2023 - Detailed explanation for encoder of U-Net

on December 20, 2023

AI Models Computer Vision Education Weekly topic

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Lab's weekly topic - week 49, 2023 - What's limitations and possible solutions for a new AI tools to detect blood poisoning?

on December 11, 2023

Big AI Weekly topic

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Lab's recommendation: Awesome books to learn machine learning and AI (continue updating)

on August 01, 2022

AI Models Education

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Python short course: Stack in python

on July 31, 2022

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Python short course: Stack and queue

on July 30, 2022

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Python short course: List in Python

on July 29, 2022

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Python short course: Thoughts about data structure

on July 28, 2022

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Python short course: Big O for algorithm analysis

on July 27, 2022

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Python short coures: Array and linked list

on July 26, 2022

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Python short course: Tree in Data structure

on July 25, 2022

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Python short course: Generator in Python

on July 24, 2022

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AI in news: AI is used to save lives with sepsis.

on July 24, 2022

Research Solution

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Research from the Lab's Little AI Scholars: Learn the life language using artificial intelligence

1. Background 1.1 project/research background This project explores the applications of Artificial intelligence (AI) techniques for classifying Deoxyribonucleic Acid (DNA) sequences by three high school students under teaching and supervision in the Lab's Little AI Scholars program. To explain AI information and concepts understandably, a couple of analogies were introduced during the research. They were displayed using interesting images to give the high school students a better understanding, and we have successfully achieved our goal of Auto Recognition of DNA Sequences. We first transformed the DNA sequences into human-like language. Then we employed Natural Language Processing (NLP) and Multi-layer perceptron (MLP) to complete sequence classification into 7 gene families from 3 organisms (humans, dogs, and chimpanzees). During this exciting research, the high school students deeply understood the biological and mathematical knowledge they learned in class and adapted them to t...

Lab's recommendation: Awesome books to learn machine learning and AI (continue updating)

Machine Learning in Action (MLiA) This is an old book, which was published ten years ago, in 2012. The author is Peter Harrington. At that time, there is not so popular ML tools like scikit learn or the DL framework like TF, and Pytorch. This book describes the ML concept and the necessary formula to understand the learning algorithms. Most importantly, the author gives the implementation bit by bit (not just call API) for the algorithms. These can get the reader a deep understanding of how the models/algorithms work and are built. It's super helpful! This book is good for beginner or intermediate readers. But please forget that the top 10 models in ML as the book says, actually there is no best one but just a suitable one. Also, there is a GitHub repo for the code (though I think could dirt your hands following the book and get the best learning). The code is in python 2, a popular version a decade ago (currently has stopped the support to python 2). I do not te...

Python short course: Stack and queue

Stack is a first in, last out data structure, while the queue is first in, first out.

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