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model.parameters() It has stored W, b. It transferable to optimizer. Let's create 1 dim of input and 1 dim of output linear model. model = nn.Linear(1,1) print(model) >>> Linear(in_features=1, out_features=1, bias=True) And check it out the Weight and Bias. First value is W, second value is b. Both are subject to learning(requires_grad=True). print(list(model.parameters())) >>> [Parameter contai..

1. Supervised Learning 1-1. Classification -Binary Classification -Multi-class Classification -Decision Trees 1-2. Regression -Linear Regression 1) Simple Linear Regression 2) Multiple Linear Regression -Polynomial Regression -Logistic Regression 2. Unsupervised learning 2-1. Clustering -K-Means Clustering -Hierarchical Clustering -DBSCAN (Density-Based Spatial Clustering of Applications with No..

Step 1. Devide the sentences into words. 2. Vectorize each words. 3. Set up the batch size. Let's say here is data. [[나는 사과를 좋아해], [나는 바나나를 좋아해], [나는 사과를 싫어해], [나는 바나나를 싫어해]] 1. Devide the sentences into words. [['나는', '사과를', '좋아해'], ['나는', '바나나를', '좋아해'], ['나는', '사과를', '싫어해'], ['나는', '바나나를', '싫어해']] 2. Vectorize each words. '나는' = [0.1, 0.2, 0.9] '사과를' = [0.3, 0.5, 0.1] '바나나를' = [0.3, 0.5, 0.2]..

validation_data it shows the accuracy whether training is going well or no, it doesn't train validation data. Once loss is turn from goes down to goes up, it is a signal of overfitting. model=Sequential() model.add(Dense(3,input_dim=4, activation='softmax', input_shape=X_train[0].shape)) model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['accuracy']) history=model.fit(X_tra..

Regression VS Classification ML Regression Classification Linear Regression Logistic Regression Cost function MSE Cross Entropy Classification Example Function Loss Linear Regression mse Binary Classification input : x output : yes or no Sigmoid nn.BCELoss() Multi-class Classification input : x or multiple x output : a or b or c... Softmax nn.CrossEntropyLoss() ML Supervised Learning Example Tra..