This repository contains my personal study notes and code implementations for various deep learning concepts. The content is based on the book "Everybody's Deep Learning, 3rd Edition" by Taeho Cho.
The main purpose of this repository is to document my learning journey and provide practical examples for each topic. The code is implemented using Python with popular deep learning frameworks.
Deep Learning Basics: Fundamental concepts of deep learning, including linear regression, neural networks, activation functions, and optimization algorithms.
Convolutional Neural Networks (CNN): Study and implementation of CNNs for image classification and other computer vision tasks.
Generative Adversarial Networks (GAN): Exploration of GANs for generating new data instances that resemble the training data.
Explainable AI (XAI): Understanding methods to interpret and explain the predictions of deep learning models.
The material and concepts in this repository are based on the following book:
Title: Everybody's Deep Learning, 3rd Edition (모두의 딥러닝(3판))
Author: Taeho Cho (조태호)