Portfolio

1. OCR implementation, multi-class classification (Coursera)

data set sample

training set prediction (1)

training set prediction (2)

https://github.com/ino-jeong/Portfolio/tree/master/OCR(multiclass_classification)

Training set accuracy : 94.86%

Octave(추천) 또는 Matlab에서 main.m 실행

구현환경 : GNU Octave 3.8, Mac OS

Coursera Machine Learning 과정 구현 과제

Training set : 20 X 20 pixel, grayscale, 5000 examples of handwritten digits

Model : Multi-class classification

Cost function 및 Training / Prediction 과정 구현 :

- lrCostFunction.m
- oneVsAll.m
- predictOneVsAll.m

2. OCR implementation, neural-net (Coursera)

training set prediction (1)

training set prediction (2)

https://github.com/ino-jeong/Portfolio/tree/master/OCR(neural_net)

Training set accuracy : 95~96% (up to random initialization)

Octave(추천) 또는 Matlab에서 main.m 실행

구현환경 : GNU Octave 3.8, Mac OS

Coursera Machine Learning 과정 구현 과제

Training set : 20 X 20 pixel, grayscale, 5000 examples of handwritten digits (1번과 동일 set)

Model : Neural Net, 3 layer (1 hidden layer)

Layer 구성 및 backpropagation 구현 :

- sigmoidGradient.m
- nnCostFunction.m

3. MNIST with CNN implementation

test set prediction (1)

test set prediction (2)

https://github.com/ino-jeong/Portfolio/tree/master/MNIST_CNN

Test set accuracy : 98.39% ~ 98.67% (if number of epoch is increased)

구현환경 : Python 3.5 with Tensorflow 1.1, Mac OS

CNN을 통한 MNIST classifier 구현 (하기 reference 참조) :

- Tensorflow official tutorial

https://www.tensorflow.org/get_started/mnist/pros
- 'DeepLearningZeroToAll' lecture by prof. Sunghun-Kim

http://hunkim.github.io/ml/

Training set : as per MNIST specification (28 X 28 pixel, grayscale)

Model : Convolution Neural Network :

- 1st layer :
convolution with 3x3 filter, 1 channel in / 32 channel out → ReLu → Max-Pooling with 2x2 filter
- 2nd layer :

convolution with 3x3 filter, 32 channel in / 64 channel out → ReLu → Max-Pooling with 2x2 filter
- 3rd later :
Fully connected layer

4. K-means clustering (Coursera)

https://github.com/ino-jeong/Portfolio/tree/master/k_means

Octave(추천) 또는 Matlab에서 main.m 실행

Basic k-means clustering implementation

구현환경 : GNU Octave 3.8, Mac OS

Coursera Machine Learning 과정 구현 과제

Model : K-means

K-means clustering algorithm 구현 (finding 3 clusters in examples) :

- computeCentroids.m
- findClosestCentroids.m
- kMeansInitCentroids.m

5. Soft Robotic Gripper Fabrication

attached on robotic arm

robot grip test

Robot arm :

- (Kuka) KR 6 R900 sixx KR AGILUS

Soft robotic gripper :

- Material : Ecoflex 00-30 silicone
- Dimension : W 135mm x L 135mm x H 10 mm

Air powered

Mold : 3d printed (by ultimaker), pla

Office paper used for inelastic side (inner side) constrainer