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Deep Learning & Machine Learning

Deep-rooted deep learning

Let's learn the principles properly by implementing algorithms from scratch without a deep learning library :)

(5.0) 10 reviews

594 learners

  • GIST-ACSL
이론 중심
연습문제
Deep Learning(DL)
Machine Learning(ML)
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What you will learn!

  • How Deep Learning Algorithms Work

  • Implementation of a seamless deep learning algorithm

  • Examples of various major deep learning model applications

From beginner to intermediate
A lecture that covers the fundamentals of artificial intelligence


Transformer, CNN, RNN... Are these models you've already heard of?
Well then, let me ask you a question.

"Which model, a CNN or a Transformer of the same size, requires more training data?"

If you were asked this question in an interview, would you be able to answer it satisfactorily? 🤔


It's not difficult once you know the basics 🎯

The world of deep learning is constantly evolving, with new models constantly emerging. However, at the heart of this change lie core concepts that remain constant . This lecture explores these core concepts , laying the foundation for a deep understanding of deep learning.

Through this course, you'll learn how to implement key models from the ground up, with clear, statistically-based explanations! Once you've mastered the core models, you'll be able to easily implement and apply other models. New deep learning models are constantly emerging, but they often build upon and adapt existing models, so a thorough understanding of the core models is crucial .


📖 To make learning easy and fun

Incorporating implementation (hands-on projects) into the course curriculum presents challenges for educators. Implementation requires a variety of elements, including environment setup, debugging, and version management. However, the sheer amount of effort spent on preparatory work can distract students from learning, and sometimes even lead to abandoning the course midway.

To minimize these difficulties , all the code used in the class is provided through Colab , so you can take the class without any environment restrictions as long as you have an internet browser .

We also provide various learning materials.

Over 70 rich lecture slides cover the model's principles in detail.
Live coding allows you to gain a deeper understanding of the coding implementation process.
We provide practice problems so you can check your learning on your own.

Lecture slides

Colab practice code


I hope you'll complete my lecture and become familiar with artificial intelligence. 💪

I recommend this to these people

Beginners learning artificial intelligence for the first time

This course covers all the various modules of deep learning and covers the fundamentals, making it the perfect choice for those who want to learn from the ground up.

Shallow Learner

What is batch normalization, and why is it necessary? Can you give clear answers to these questions? If you've encountered deep learning but find it challenging, try solidifying your understanding of the core concepts!

After class

  • You can properly understand the basic concepts of deep learning.

    • You can learn basic concepts through implementation by implementing the basic elements of deep learning, such as backpropagation and regularization, using only the numpy library without a platform such as Pytorch.

  • You can fully understand major deep learning models such as CNN, RNN, Seq2Seq, Word Vector, and Transformer through conceptual depth and ground-up implementation.

Features of this course



Deep Learning: Learning from the Ground Up

The basic elements of deep learning can be learned using only the numpy library.
You can learn the core concepts by implementing them.



A complete theoretical explanation based on statistics

Since deep learning is a statistics-based technology, basic statistical knowledge is required.
Through this, you can accurately understand deep learning models.

Who created this course


Former PhD and researcher at Korea Advanced Institute of Science and Technology (KAIST)

Current) Professor at Gwangju Institute of Science and Technology (GIST)


Things to note before taking the course

Learning Materials

  • Each lesson slide and Colab link are provided.

Player Knowledge and Precautions

  • Even if you can only do basic Python implementation, you can follow the class.

  • All exercises will be done in Colab to make setup as easy as possible.


  • We strongly recommend that you follow the provided Colab code and complete the practice problems. This course is designed to deepen your understanding of the theory through hands-on practice.

Recommended for
these people

Who is this course right for?

  • For those who are new to deep learning

  • For those who want to grasp both implementation and theory

  • Researchers/developers who want to properly establish the basics

  • Those who want to understand the principles by implementing them thoroughly from the ground up

Hello
This is

594

Learners

10

Reviews

5.0

Rating

1

Course

안녕하세요. 로봇AI를 연구하는 광주과학기술원 AI대학원 김의환입니다.

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연구 관련 더 자세한 내용은 GIST ACSL 홈페이지를 참조해주세요.

앞으로 여러분에게 도움이 되는 강의로 만나겠습니다 :)

Curriculum

All

77 lectures ∙ (24hr 28min)

Published: 
Last updated: 

Reviews

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10 reviews

5.0

10 reviews

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