- This repo contains the lesson in GonKen
- Introduction
- Learning Algorithm
- How to Evaluate Your Model
- Overfitting and Underfitting
- Practice: Overfitting and Underfitting
- Bias and Variance / Hyperparameter and Validation Set
- Maximum Likelihood Estimation
- Practice: Unsupervised and Supervised learning
- Introduction of Deeplearning
- Gradient-Based Learning and SGD
- Backpropagation
- Practice: Neural Network Instruction with PyTorch
- Regularization 1
- Regularization 2
- Practice: Overfitting and Underfitting
- Practice: Regularization
- What are AI, machine learning, deeplearning.
- Why we should use deeplearning in certain tasks
- 5.1
- discrete example for machine learning algorithm
- 11.1
- 5.2
- Linear/Poly Regression model
- Decision Tree
- 5.3
- 5.4.4
- math excercise
- unsupervised: PCA, Clastering(k-means), KDE
- supervised: random forest, logistic regression, kNN, NN
- 6
- 4.3(no 4.3.1), 5.9
- 6.3.1
- Why ReLU is employed as activation function in DNN
- Build simple structure of NN in PyTorch
- 7.1, 7.4, 7.5, 7.8
- L1 and L2
- 8.1.3, 8.7.1
- train in dataset which is easy to be overfitting/underfitting
- to prevent overfitting