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모두를 위한 딥러닝 시즌 2 (Deep learning zero to all - PyTorch)

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1-1. Tensor Manipulation 1.md
1-1. Tensor Manipulation 1.md
 
 
1-2. Tensor Manipulation 2.md
1-2. Tensor Manipulation 2.md
 
 
1. Docker Instruction.md
1. Docker Instruction.md
 
 
10-0. Convolution Neural Networkintro.md
10-0. Convolution Neural Networkintro.md
 
 
10-1. Convolution.md
10-1. Convolution.md
 
 
10-2. MNIST CNN.md
10-2. MNIST CNN.md
 
 
10-3. visdom.md
10-3. visdom.md
 
 
10-4-1. ImageFolder1.md
10-4-1. ImageFolder1.md
 
 
10-4-2. ImageFolder2.md
10-4-2. ImageFolder2.md
 
 
10-5. Advance CNN(VGG).md
10-5. Advance CNN(VGG).md
 
 
10-6-1. Advanced CNN(RESNET)1.md
10-6-1. Advanced CNN(RESNET)1.md
 
 
10-6-2. Advanced CNN(RESNET)2.md
10-6-2. Advanced CNN(RESNET)2.md
 
 
10-7. Next step of CNN.md
10-7. Next step of CNN.md
 
 
11-0. RNN intro.md
11-0. RNN intro.md
 
 
11-1. RNN basics.md
11-1. RNN basics.md
 
 
11-2. RNN hihello and charseq.md
11-2. RNN hihello and charseq.md
 
 
11-3. RNN Long sequence.md
11-3. RNN Long sequence.md
 
 
11-4. RNN timeseries.md
11-4. RNN timeseries.md
 
 
11-5. RNN seq2seq.md
11-5. RNN seq2seq.md
 
 
11-6. RNN PackedSequence.md
11-6. RNN PackedSequence.md
 
 
2. Linear regression.md
2. Linear regression.md
 
 
3. Deeper Look at GD.md
3. Deeper Look at GD.md
 
 
4-1. Multivariable Linear regression.md
4-1. Multivariable Linear regression.md
 
 
4-2. Loading Data.md
4-2. Loading Data.md
 
 
5. Logistic Regression.md
5. Logistic Regression.md
 
 
6. Softmax Classification.md
6. Softmax Classification.md
 
 
7-1. Tips.md
7-1. Tips.md
 
 
7-2. MNIST Introduction.md
7-2. MNIST Introduction.md
 
 
8-1. Perceptron.md
8-1. Perceptron.md
 
 
8-2. Multi Layer Perceptron.md
8-2. Multi Layer Perceptron.md
 
 
9-1. ReLU.md
9-1. ReLU.md
 
 
9-2. Weight initialization.md
9-2. Weight initialization.md
 
 
9-3. Dropout.md
9-3. Dropout.md
 
 
9-4. Batch Normalization.md
9-4. Batch Normalization.md
 
 
README.md
README.md
 
 

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Deep_learning_zero_to_all-PyTorch


1. README

  • 이 Repository는 '모두를 위한 딥러닝 시즌 2 - PyTorch'의 내용을 이해하기 쉽게 요약하고 실습코드를 따라 작성한 내용이 담겨져 있습니다.
  • 이 Repository는 SAI-Summer_Study를 위해 공부한 내용을 기록했습니다.
  • This repository includes theories, studies and codes about 'Deep learning zero to all - PyTorch'
  • Original Address : https://deeplearningzerotoall.github.io/season2/lec_pytorch.html

2. Contents

PART 1: Machine Learning & PyTorch Basic

  • Lab-01-1 Tensor Manipulation 1
  • Lab-01-2 Tensor Manipulation 2
  • Lab-02 Linear regression
  • Lab-03 Deeper Look at GD
  • Lab-04-1 Multivariable Linear regression
  • Lab-04-2 Loading Data
  • Lab-05 Logistic Regression
  • Lab-06 Softmax Classification
  • Lab-07-1 Tips
  • Lab-07-2 MNIST Introduction

PART 2: Neural Network

  • Lab-08-1 Perceptron
  • Lab-08-2 Multi Layer Perceptron
  • Lab-09-1 ReLU
  • Lab-09-2 Weight initialization
  • Lab-09-3 Dropout
  • Lab-09-4 Batch Normalization

PART 3: Convolutional Neural Network

  • Lab-10-0 Convolution Neural Networkintro
  • Lab-10-1 Convolution
  • Lab-10-2 mnist cnn
  • Lab-10-3 visdom
  • Lab-10-4-1 ImageFolder1
  • Lab-10-4-2 ImageFolder2
  • Lab-10-5 Advance CNN(VGG)
  • Lab-10-6-1 Advanced CNN(RESNET-1)
  • Lab-10-6-2 Advanced CNN(RESNET-2)
  • Lab-10-7 Next step of CNN

PART 4: Recurrent Neural Network

  • Lab-11-0 RNN intro
  • Lab-11-1 RNN basics
  • Lab-11-2 RNN hihello and charseq
  • Lab-11-3 Long sequence
  • Lab-11-4 RNN timeseries
  • Lab-11-5 RNN seq2seq
  • Lab-11-6 PackedSequence

3. Learn More

About

모두를 위한 딥러닝 시즌 2 (Deep learning zero to all - PyTorch)

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