머신러닝 / 딥러닝
- 딥러닝은 무엇인가요? 딥러닝과 머신러닝의 차이는?
- deep learning is a mimic of the human brain.
- There are a lot of types of machine learning methods such as Logistic regression, SVM, tree model, random forest. Deep Learning is one of them.
- 왜 갑자기 딥러닝이 부흥했을까요?
- The quantity of data has increased so much after the era of smartphone arrive.
- But the Data we faced these days are different from the traditional situation.
- Structure of the Data has been changed such as video, audio, and photos
- Cost Function과 Activation Function은 무엇인가요?
- Cost function, aka loss function, quantifies the error between the predicted values and actual value. ex. MSE
- Activation function tells whether a neuron should be activated or not. They allow the model to learn more complex pattern. ex. ReLU, Sigmoid, Tan-hyperbolic..
- 오버피팅일 경우 어떻게 대처해야 할까요?
- There are 3 ways to handle overfitting.
- First, simplify the model by using L1 or L2 regularization technique
- Second, use cross-validation techniques
- Last, pruning or dropout can be used. We can achieve a balance between bias and variance
- 하이퍼파라미터는 무엇인가요?
- parameters, that are not learned from the data.
- play a crucial role in determining the behavior of the training algo.
자연어처리
- One-Hot 인코딩에 대해 설명해주세요
- It is the method to resolve categorical data.
- Each categories are
- POS 태깅은 무엇인가요?
- a process in NLP, clarify the meaning of a word in its specific content
- the accuracy and reliability of POS tagging can significantly impact the performance of NLP.
- RNN / LSTM 설명
- RNN is a type of Neural Network that is used for sequential data. Unlike feed-forward NN, they have loops within so that the info can persist. Key feature : captures info about previous state.
- LSTM is a special kind of RNN, specifically designed to overcome the problem of vanishing gradient. They achieve it by components called gates.