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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.

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

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