The lectures are kind and the teacher is easy-going~~ Thank you. Please keep making lectures!@~

erickim

This newly designed course, drawing from the instructor's initial deep learning failures, helps you progressively learn deep learning essentials: math, theory, PyTorch-based implementation, transfer learning, and GPT's core Transformer.

funcoding

2025 추석맞이 감사할인

Deep Learning and PyTorch Bootcamp for Beginners (Easy! From Basics to ChatGPT Core Transformer) [Data Analysis/Science Part3]

I will work hard to hone my experience and skills. Thank you^^

Compared to the teacher's hard work, the comments seem too insincere, so I'll write a few more words.
If you watch this lecture, you can see that he really put in a lot of effort.
* From video editing to audio volume, video flow, and messages, he made the lectures one by one so that they would be smooth. (When you watch YouTube, you feel a lot of awkward editing... there's none.)
* I can feel that he put a lot of thought into approaching theory and coding, so the lectures feel really easy.
If there are more lectures from the teacher in the future, I'm confident that I'll listen to them without hesitation!!
Thank you.

입문

초급

중급이상

Deep Learning(DL)

파이토치

PyTorch

Machine Learning(ML)

Artificial Neural Network

비전 트랜스포머

Vision Transformer

Introduction to Lectures and Course Progress

Essential Basic Background Knowledge (Theory) for Understanding Deep Learning Concepts

PyTorch Basic Syntax Summary (Hands-on Focused)

Summary of Numpy library usage necessary for deep learning implementation (practice-oriented)

PyTorch Tensor Operations: Math & Implementation (Theory+Practice)

Understanding Core Deep Learning Theory (Theory-focused)

Deep Learning Basic Structure Implementation (Hands-on)

Implementing Deep Learning Training (Hands-on)

Summary of Various Activation Functions (Theory + Practice)

Organizing Various Optimizers (Theory-focused)

Applying Deep Learning to Classification Problems

Improving Deep Learning Architecture (Hands-on)

Deep Learning Regularization Methods Summary (Theory)

Deep Learning Basics for Images: Understanding CNN (Theory + Practice)

Deep Learning Fundamentals for Sequential Data: Understanding RNN and LSTM (Theory Focus)

Deep Learning Basics for Sequential Data: Applying RNN and LSTM (Hands-on)

Latest Deep Learning Shortcut: Understanding Transfer Learning

Key Understanding of Major Image Models for Transfer Learning (Theory-focused)

Intermediate Deep Learning: Applying the Latest Vision Transformer Model using Transfer Learning (Hands-on focused)

Intermediate Deep Learning: Solving Real-World Problems with Kaggle Challenges

Outro