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machinelearning's Introduction

Tiến độ học Machine Learning

  • Linear Regression

    • Cost Function
    • Gradient Descent
    • Feature Scaling
    • Vectorization (SIMD)
    • Logistic Regression
    • Overfitting, Underfitting
    • Regularization
  • Neural Network

    • Neural Network Forward Propagation
    • Numpy library
    • Tensor Flow, PyTorch or Pure Python?
    • Linear Algebra(vector and matrix)
    • Activation Function alternative to Sigmoid
    • Multi-class Classification
    • Soft Max
    • Adam (introduce)
    • Other Layer types (Convolutional)
  • What 's a good model?

    • Test set, Dev set
    • Model selection
    • Probility and statistics
    • Baseline, Bias, Variance review
    • Learning curve
  • What to try when model is not good?

    • More training data (Fix high variance)
    • Try smaller set of features (Fix high variance)
    • Get additional features (Fix high bias)
    • Add polynomial features (Fix high bias)
    • Decrease regularization parameter (Fix high bias)
    • Increase regularization parameter (Fix high variance)
    • Neural network often low bias, so just add more data
  • How to collect more data?

    • Augmentation (transform x -> x' but still same y)
    • Tranfer learning
  • ML development process

  • Unethical side of ML

  • Skewed dataset, precision, recall, F1 score

  • Desision tree

    • When to stop spliting?
    • Impurity, Entropy
    • Information gain
    • One hot encoding (split feature)
    • Continuous feature
    • Regression decision tree (use variance)
    • Tree ensemble
    • Sample replacement, random forest, XGBoost
  • Unsupervised learning

    • K-means, Elbow method
    • Anomaly detection
      • Normal distribution
      • Choosing epsilon
  • Recommended system

    • Collaborative filtering
    • Content based filtering
  • Reinforcement learning (learnt in AI class this semester)

    • Markov Decision Process
    • Q-learning
    • Bellman equation
    • Continoues State Space
    • Neural Network for Q-learning
    • Mini batch, soft update

Deep learning

  • Computation graph
  • Derivative chain rule (Đạo hàm của hàm hợp)
  • Vectorization
  • Broadcasting in numpy
  • Regularization
  • Drop out regularization
  • Normalization (từ xác suất thống kê)
  • Vanishing/ Exploding gradient (very deep network problem)
  • Gradient checking
  • Mini batch
  • Exponentlly weighted average (trung bình động)
  • Bias correction
  • Gradient descent with momentum
  • RMSprop
  • Adam optimization
  • Learning rate decay
  • Tuning hyperparameters (learning rate > beta > mini batch size > hidden layer size > number of layers)
  • Sampling scale
  • Pandas vs Caviar approach
  • Batch normalization (normalize z of layer[i])
  • Human Performance, Bayes Optimal Error
  • Tunning model for performance
  • Data mismatch
  • Train-Dev set
  • Multitask and transfer learning
  • End-to-end deep learning
  • Convolutional Neural Network
  • Max pooling, Average pooling

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