<혼자 공부하는 머신러닝 + 딥러닝>을 통해 공부한 실습 자료 리포지토리입니다.
- 9.25 1회독
- 01_3: Binary Classfication using KNN
- 02_1: Train Set and Test Set Split
- 02_2: Data Preprocessing: Scaling
- 03_1: Regression using KNN
- 03_2: Linear Regression
- 03_3: Feature Engineering and Regularization
- 04_1: Multiple Regression, Logistic Regression (Sigmoid & Softmax)
- 04_2: Stochastic Gradient Descent, Loss Function, Epoch
- 05_1: Decision Tree (+ Gini Impurity)
- 05_2: Cross Validation & Grid Search
- 05_3: Tree Ensemble (Random Forest, Extra Tree, Gradient Boosting, Histogram-based Gradient Boosting)
- 06_1: Unsupervised Learning: Clustering
- 06_2: KMeans (+ Elbow using Inertia)
- 06_3: PCA(Principal Components Analysis), Dimension Reduction
- 07_1: ANN, Structure of ANN (Tensorflow and Keras)
- 07_2: DNN, relu (activation function for Image Classification), Optimizer(RMSprop, Adam, .. etc)
- 07_3: ANN Model Training (history, validation set loss, dropout, callback(ModelCheckpoint, EarlyStopping))
- 08_2: CNN Modeling (Filter, Kernel, Padding, Stride, Pooling)
- 08_3: CNN Visualization (Filter, Feature Map)
- 09_2: RNN Modeling (One-hot Encoding, Word Embedding)
- 09_3: RNN Modeling 2 (LSTM, GRU, Dropout, 2-RNN)