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This is a newly designed course that allows you to gradually learn the mathematics, theory, PyTorch-based implementation, transfer learning, and GPT's core transformer needed to understand deep learning, based on the instructor's own failed experiences when first learning deep learning.

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Deep Learning and PyTorch Bootcamp for Beginners (Easy! From Basics to ChatGPT's Core Transformer) [Data Analysis/Science Part 3]

입문

초급

중급이상

Deep Learning(DL)

파이토치

PyTorch

Machine Learning(ML)

Artificial Neural Network

비전 트랜스포머

Vision Transformer

Introduction to Lectures and Course Curriculum

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

PyTorch Basic Syntax Summary (Practice-Oriented)

A Summary of Numpy Library Usage for Deep Learning Implementation (Practice-Focused)

PyTorch Tensor Operations: Mathematical Theory and Implementation Summary (Theory + Practice)

Understanding Core Deep Learning Theory (Theory-Focused)

Implementing Basic Deep Learning Architecture (Practice-Focused)

Implementing Deep Learning Training (Practice-Focused)

Summary of Various Activation Functions (Theory + Practice)

Understanding Various Optimizers (Theory-Focused)

Applying Deep Learning to Classification Problems

Improving Deep Learning Architecture (Practice-Focused)

Summary of Deep Learning Regularization Techniques (Theory-Focused)

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

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

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

The Shortcut to Leveraging the Latest Deep Learning Technology: Understanding Transfer Learning

Understanding Key Image Models for Transfer Learning (Theory-Focused)

Deep Learning Intermediate: Applying State-of-the-Art Vision Transformer Models with Transfer Learning (Hands-on Practice)

Intermediate Deep Learning: Solving Real Problems with Kaggle Competitions

Outro