2025.02.07 [Deep Learning] 10. 전이 학습(Transfer Learning)과 ResNet Deep Learning AI deep learning neural network
2025.02.06 [Deep Learning] 9. 합성곱 신경망(Convolutional Neural Network, CNN) 소개 Deep Learning AI CNN computer vision deep learning ImageNet neural network pytorch ResNet transfer learning
2025.02.06 [Deep Learning] 8. 딥러닝 모델의 여러 가지 학습 개선 방법 Deep Learning AI deep learning dropout hyperparameter neural network overfitting parameter training methods weight decay
2025.02.06 [Deep Learning] 7. 배치 정규화(Batch Normalization) Deep Learning AI batch normalization formula batch normalization deep learning neural network
2025.02.06 [Deep Learning] 6. 가중치의 초기값을 설정하는 방법 Deep Learning AI deep learning neural network weight initialization methods weight initialization
2025.02.05 [Deep Learning] 5. 매개변수를 갱신하는 다양한 방법 Deep Learning AI deep learning gradient descent neural network optimizer stochastic gradient descent
2025.02.05 [Deep Learning] 4. 오차 역전파(Error Backpropagation)에 대해 알아보자 Deep Learning AI backpropagation deep learning error backward propagation gradient descent loss function neural network training neural network
2025.02.04 [Deep Learning] 2. MNIST 데이터셋을 활용한 신경망 구현 Deep Learning AI deep learning deep learning MNIST neural network
2025.02.04 [Deep Learning] 3. 신경망 학습(Neural Network Training)에 대해 알아보자 Deep Learning AI backpropagation deep learning deep learning gradient descent loss function neural network training neural network
2025.02.03 [Deep Learning] 1. 신경망(Neural network) 기초 Deep Learning activation function AI deep learning deep learning neural network relu sigmoid softmax