Intro-to-Deep-Learning-with-PyTorch

Udacity Free Course인 Intro-to-Deep-Learning-with-PyTorch 과정을 진행하면서 발생되는 소스코드나 학습내용들을 기록하기 위해 만들었습니다.

Secure and Private AI Scholarship에 선발되어 해당 내용과 유사한 Nanodegree 프로그램을 진행하기 때문에 해당 Repository는 더 이상 업데이트 되지 않습니다.

Bertelsmann Scholarship 프로그램이 위와 비슷한 커리큘럼을 가지고 있어 해당 Repository에 기록.

학습 일지

• 2019-01-01
  • Lesson 2-10 (Perceptron-Algorithm)
• 2019-01-02
  • Lesson 2-17 (Maximum-Likelihood)
• 2019-01-03
  • Lesson 2-20 (Cross-Entropy 2)
• 2019-01-05
  • Lesson 2-26 (Gradient-Descent)
• 2019-01-06
  • Lesson 2-35 (Analyzing-Student-Data)
• 2019-01-07
  • Lesson 2-50 (Error-Functions) Lesson2 Finish
• 2019-01-08
  • Lesson 4-2 (Single-Layer-Neural-Networks) Lesson3 Finish
• 2019-01-09 ~ 2019-01-12
  • Review & Read a Book (Deep Learning from Scratch)
• 2019-01-13
  • Lesson 4-10 (Network-Architectures Solution)
• 2019-01-14
  • Lesson 4-22 Lesson4 Finsih
• 2019-01-15
  • Review
• 2019-01-16
  • Review
• 2019-01-17
  • Back to Lesson 4-9 (I don't understand)
• 2019-01-21
  • Repeat Lesson 4-10
• 2019-01-22
  • Repeat Lesson 4-11
• 2019-01-23
  • Repeat Lesson 4-13
• 2019-01-24
  • Making Test Data-Set
• 2019-01-26
  • Finish My Own Data-Set
• 2019-01-28
  • Repeat Lesson 4-16
• 2019-01-29
  • Repeat Lesson 4-19
• 2019-01-30
  • Implementing Classify Dog-vs-Cat
• 2019-01-31
  • Still doing(Implementing...)
• 2019-02-01
  • Implement Dog-vs-Cat (accuracy - 0.5)
• 2019-02-02
  • Change Model for Accuracy
• 2019-02-07
  • Doing Part8
• 2019-02-08
  • Lesson4 Finish
• 2019-02-09
  • New Setting for CUDA
• 2019-02-20
  • Temporarily suspend for important projects. (Until 2019-03-01)
• 2019-03-08
  • MLP Structure & Class Score
• 2019-03-10
  • Change Anaconda Env for New GPU
• 2019-03-11
  • Loss & Optimize
• 2019-03-12
  • Training the Network
• 2019-03-13
  • One Solution
• 2019-03-14
  • Model Validation
• 2019-03-15
  • Repeat Lesson 5-3
• 2019-03-16
  • Repeat Lesson 5-5
• 2019-03-17
  • Repeat Lesson 5-6
• 2019-03-18
  • Repeat Lesson 5-8
• 2019-03-22
  • Temporarily suspend for other study. (Until 2019-03-25)
• 2019-03-28
  • Review
• 2019-03-29
  • Review 2
• 2019-03-30
  • Review 3
• 2019-03-31
  • Validation Loss
• 2019-04-01
  • Image Classification Steps
• 2019-04-02
  • MLPs vs CNNs
• 2019-04-04
  • Local Connectivity
• 2019-04-05
  • Filters and the Convolutional Layer
• 2019-04-06
  • Filters & Edges
• 2019-04-07
  • Frequency in images
• 2019-04-08
  • High-pass Filters
  • Temporarily suspend for other project (Until Undetermined)
• 2019-04-16
  • OpenCV & Creating Custom Filters
• 2019-04-17
  • Convolutional
• 2019-05-12
  • Stride & Padding
• 2019-05-14
  • CNNs in PyTorch
• 2019-05-20
  • Pooling Layers
• 2019-05-22
  • Capsule Networks
• 2019-05-23
  • Increasing Depth
• 2019-05-24
  • CNNs for Image Classification
• 2019-05-25
  • Convolutional Layers in PyTorch
• 2019-05-26
  • Feature Vector
• 2019-05-27
  • CIFAR Classification
• 2019-05-28
  • Image Augmentation
• 2019-05-29
  • Augmentation Using Transformations
• 2019-05-30
  • Groundbreaking CNN Architectures
• 2019-05-31
  • Visualizing CNNs
• 2019-06-01
  • Summary of CNNs

Quiz-Script

• 2019-01-01
  • perceptron-algorithm-quiz
• 2019-01-02
  • softmax-function-quiz
• 2019-01-03
  • cross-entropy-quiz

Implement-Script

• 2019-01-05
  • gradient-descent-algorithm
• 2019-01-06
  • predicting-neural-network

Jupyter-Notebook

• 2019-01-17
  • Part1 (Lesson 4-1 ~ 4-4)
  • Part2 (Lesson 4-5 ~ 4-8)
• 2019-01-22
  • Part3 (Lesson 4-9 ~ 4-11)
• 2019-01-25
  • Part4 (Lesson 4-12 ~ 4-13)
• 2019-01-27
  • Data-Set
• 2019-01-28
  • Part5 (Lesson 4-14 ~ 4-16)
• 2019-01-29
  • Part6 (Lesson 4-17)
  • Part7 (Lesson 4-18 ~ 4-19)
• 2019-02-01
  • Dog-vs-Cat
• 2019-02-08
  • Part8 (Without CUDA)