Joongheon Kim

3. Quantum Computing Basics – Quantum Gates and Quantum Circuits

4. Understanding Quantum Neural Networks

2. Quantum Computing Basics – Qubits and Quantum States

1. Understanding Artificial Neural Networks

5. Quantum Reinforcement Learning and Mobility Applications

퀀텀딥러닝(Quantum Deep Learning): Quantum Federated Learning

[랜덤변수] Discrete Random Variables

Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks

강화학습 기술동향

Advanced Deep Learning Methods for Autonomous Mobility

Introduction to Lyapunov Optimization (7 min)

심층강화학습(Deep Reinforcement Learning) 제1강. 배경, 목적, Q-Learning, MDP, DQN

Multi-Agent Deep Reinforcement Learning for UAV-based Services (IETF Forum, 10/15/2020)

고려대학교 AI and Mobility Laboratory 연구 소개 (2021년09월)

딥러닝과 데이터과학

[C언어] Linked List, 예제로 이해하기

[C언어] 포인터, 예제로 이해하기

강화학습기초(Introduction to Reinforcement Learning)

Multi-Agent Deep Reinforcement Learning for Connected and Autonomous Vehicles (ICAIIC 2021)

Markov Chain [마코프 체인] (서론, Transition Diagram/Matrix, Multi-Step Transition)

순천대특강(딥러닝과 데이터과학) 2. 데이터 차원 축소

순천대특강(딥러닝과 데이터과학) 1. 딥러닝 기초

Deep Learning Computation for Economic Theory and Its Applications (English)

Deep Learning Theory and Software: GAN, interpolation, overfitting, PCA/LDA

Deep Learning Theory and Software: CNN with Cifar10

Deep Learning Computation for Economic Theory and Its Applications

Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks (TWC2019)

Deep Learning Theory and Software (Lecture 01/02): 딥러닝개요, 선형회귀분석

Deep Learning Theory and Software (Lecture 02): Linear Regression

Deep Learning Theory and Software (Lecture 01), Introduction