๋ชจ๋๊ฐ ๋ง๋๋ ๋ชจ๋๋ฅผ ์ํ ๋ฅ๋ฌ๋ ์์ฆ 2์ ์ค์ ์ฌ๋ฌ๋ถ๋ค ํ์ํฉ๋๋ค.
์๋ ๋งํฌ์์ ์ฌ๋ผ์ด๋์ ์์์ ํตํด ํ์ต์ ์์ํ ์ ์์ต๋๋ค.
- Slide: http://bit.ly/2LQMKvk
- YouTube: http://bit.ly/2HHrybT
๋์ผํ ์ค์ต ํ๊ฒฝ์ ์ํด docker๋ฅผ ์ฌ์ฉํ์ค ๋ถ์ docker_user_guide.md ํ์ผ์ ์ฐธ๊ณ ํ์ธ์! :)
pip install -r requirements.txt
Deep Learning Zero to All - TensorFlow
๋ชจ๋ ์ฝ๋๋ Tensorflow 1.12(stable)๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์์ฑํ์ผ๋ฉฐ Tensorflow 2.0์ด ์ถ์๋๋ ๋๋ก ์ถํ ๋ฐ์ํ ์์ ์ ๋๋ค.
์ฝ๋๋ Tensorflow ๊ณต์ ํํ์ด์ง ๊ถ์ฅ์ ๋ฐ๋ผ Keras + Eager๋ก ์์ฑํ์ผ๋ฉฐ
Session ๋ฒ์ ์ code_session_version / Keras ๋ฒ์ ์ other์์ ํ์ธํ์ค ์ ์์ต๋๋ค.
์ธ์ ๋ ์ฌ๋ฌ๋ถ๋ค์ ์ฐธ์ฌ๋ฅผ ํ์ํฉ๋๋ค. Comments๋ Pull requests๋ฅผ ๋จ๊ฒจ์ฃผ์ธ์.
We always welcome your comments and pull requests.
- Lec 01: ๊ธฐ๋ณธ์ ์ธ Machine Learning์ ์ฉ์ด์ ๊ฐ๋ ์ค๋ช
- Lab 01: (์ถ๊ฐ ์์ )
- Lec 02: Simple Linear Regression
- Lab 02: Simple Linear Regression๋ฅผ TensorFlow๋ก ๊ตฌํํ๊ธฐ
- Lec 03: Linear Regression and How to minimize cost
- Lab 03: Linear Regression and How to minimize cost๋ฅผ TensorFlow๋ก ๊ตฌํํ๊ธฐ
- Lec 04: Multi-variable Linear Regression
- Lab 04: Multi-variable Linear Regression๋ฅผ TensorFlow๋ก ๊ตฌํํ๊ธฐ
- Lec 05-1: Logistic Regression/Classification์ ์๊ฐ
- Lec 05-2: Logistic Regression/Classification์ cost ํจ์, ์ต์ํ
- Lab 05-3: Logistic Regression/Classification๋ฅผ TensorFlow๋ก ๊ตฌํํ๊ธฐ
- Lec 06-1: Softmax Regression: ๊ธฐ๋ณธ ๊ฐ๋ ์๊ฐ
- Lec 06-2: Softmax Classifier์ cost ํจ์
- Lab 06-1: Softmax classifier๋ฅผ TensorFlow๋ก ๊ตฌํํ๊ธฐ
- Lab 06-2: Fancy Softmax classifier๋ฅผ TensorFlow๋ก ๊ตฌํํ๊ธฐ
- Lab 07-1: Application & Tips: ํ์ต๋ฅ (Learning Rate)๊ณผ ๋ฐ์ดํฐ ์ ์ฒ๋ฆฌ(Data Preprocessing)
- Lab 07-2-1: Application & Tips: ์ค๋ฒํผํ (Overfitting) & Solutions
- Lab 07-2-2: Application & Tips: ํ์ต๋ฅ , ์ ์ฒ๋ฆฌ, ์ค๋ฒํผํ ์ TensorFlow๋ก ์ค์ต
- Lab 07-3-1: Application & Tips: Data & Learning
- Lab 07-3-2: Application & Tips: ๋ค์ํ Dataset์ผ๋ก ์ค์ต
- Lec 08-1: ๋ฅ๋ฌ๋์ ๊ธฐ๋ณธ ๊ฐ๋ : ์์๊ณผ XOR ๋ฌธ์
- Lec 08-2: ๋ฅ๋ฌ๋์ ๊ธฐ๋ณธ ๊ฐ๋ 2: Back-propagation ๊ณผ 2006/2007 '๋ฅ'์ ์ถํ
- Lec 09-1: XOR ๋ฌธ์ ๋ฅ๋ฌ๋์ผ๋ก ํ๊ธฐ
- Lec 09-2: ๋ฅ๋ทํธ์ ํ์ต ์ํค๊ธฐ (backpropagation)
- Lab 09-1: Neural Net for XOR
- Lab 09-2: Tensorboard (Neural Net for XOR)
- Lab 10-1: Sigmoid ๋ณด๋ค ReLU๊ฐ ๋ ์ข์
- Lab 10-2: Weight ์ด๊ธฐํ ์ํด๋ณด์
- Lab 10-3: Dropout
- Lab 10-4: Batch Normalization
- Lec 11-1: ConvNet์ Conv ๋ ์ด์ด ๋ง๋ค๊ธฐ
- Lec 11-2: ConvNet Max pooling ๊ณผ Full Network
- Lec 11-3: ConvNet์ ํ์ฉ ์
- Lab 11-0-1: CNN Basic: Convolution
- Lab 11-0-2: CNN Basic: Pooling
- Lab 11-1: mnist cnn keras sequential eager
- Lab 11-2: mnist cnn keras functional eager
- Lab-11-3: mnist cnn keras subclassing eager
- Lab-11-4: mnist cnn keras ensemble eager
- Lab-11-5: mnist cnn best keras eager
- Lec 12: NN์ ๊ฝ RNN ์ด์ผ๊ธฐ
- Lab 12-0: rnn basics
- Lab 12-1: many to one (word sentiment classification)
- Lab 12-2: many to one stacked (sentence classification, stacked)
- Lab 12-3: many to many (simple pos-tagger training)
- Lab 12-4: many to many bidirectional (simpled pos-tagger training, bidirectional)
- Lab 12-5: seq to seq (simple neural machine translation)
- Lab 12-6: seq to seq with attention (simple neural machine translation, attention)
Main Instructor
- Prof. Kim (https://github.com/hunkim)
Main Creator
- ๊น๋ณด์ญ (https://github.com/aisolab)
- ๊น์์ (https://github.com/healess)
- ๊น์คํธ (https://github.com/taki0112)
- ์ ์ฑ์ง (https://github.com/aiscientist)
- ์ด์น์ค (https://github.com/FinanceData)
- ์ด์ง์ (https://github.com/jwlee-ml)
Docker Developer
- ์ค์์ค (https://github.com/juneoh)
Support
- ๋ค์ด๋ฒ ์ปค๋ฅํธ์ฌ๋จ : ์ดํจ์, ์ฅ์ง์, ์์ฐ๋ด