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Tensorflow 기반 딥러닝 핵심과 활용

1. 머신 러닝의 동작 원리

  • AI 문제 해결 기술
  • 머신 러닝 개요와 프로젝트 프로세스
  • 학습모델과 회귀분석 및 비용 함수의 이해
  • 오차수정과 경사하강법의 원리
  • 로지스틱 회귀와 활성화 함수의 이해
  • 소프트맥스(softmax)의 동작 이해

2. 딥러닝의 구현 방법과 활용

  • 머신러닝/딥러닝 프레임워크의 종류
  • Tensorflow를 이용한 머신러닝/딥러닝 구현
  • 딥 러닝 개요와 구현 방법의 이해
  • 딥러닝 구현 핵심 기술의 이해 - CNN, RNN/LSTM, GAN 등
  • 머신러닝/딥러닝 활용 사례와 연구동향

3. 오차 역전파의 이해와 신경망 구현

  • 계산 그래프와 연쇄 법칙 (Chain Rule)
  • 역전파 (Back Propagation)
  • Network Layer 구현
  • 오차역전파법을 적용한 신경망 구현

4. 신경망 학습의 핵심 요소

  • 데이터 전처리 (Data Processing)
  • 가중치 초기화 (Weight Initialization)
  • 활성화 함수 (Activation Functions)
  • 최적화 (Optimization)
  • Normalization & Generalization
  • Early Stopping
  • Hyperparameter 최적화 (Optimization)
  • Learning Process의 관리

부록. 머신러닝/딥러닝 실습환경 구축

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