deep_learning_for_visual_recognition's Introduction

Deep_Learning_For_Visual_Recognition (Pytorch)

Deep Learning For Visual Recognition (STA9131)

1. KNN

Implement KNN and use cross-validation to find optimal K

2. Linear_classifier

2-1. SVM loss

2-2. Softmax loss

3. Fully connected neural network

3-1. Two layer net

3-2. Fully connected layer

4. Convolutional networks

Make modules such as Convolutional layer forward, backward, batchnormalization and make deep convolutional networks.

4.1 Deep convolutional networks

Forward, backward for Convolutional layer, ReLU layer, Pooling layer, BN

4.1 Initialization

Kaiming initialization

4.1 BN (Batchnormalization)

BN
spatial BN

5. VAE

pytorch autograd and nn

6. GAN

7. Motion captioning with RNNs and LSTMs

7-1. RNNs

7-2. LSTM

https://dgkim5360.tistory.com/entry/understanding-long-short-term-memory-lstm-kr

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