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LESSON

AI Course - Week1

Read the official learning documents and study the warehouse part with Data-Science-Notes open in new window

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AI is an optional module.

DAY1-4: Preparation stage

1. Specific requirements

  1. Clone repository：

    • git clone --recursive https://github.com/fengdu78/Data-Science-Notes.git

  2. Read the [0.math] section of the warehouse to review mathematics-related knowledge：

    • linear algebra：
      • Vector and matrix operations
      • Matrix inversion
      • Matrix factorization
    • Calculus：
      • Differential
      • gradient
      • Convex functions and optimization
    • probability theory：
      • random variable
      • Distribution
      • Bayesian formula
      • Parameter estimation
      • Hypothesis testing

2. Display of learning results

  1. Sharing study notes and mind maps
  2. Practice on optional topics

3. Reference materials

  1. deep learning：
    • "Deep Learning" Yoshua Bengio & Ian GoodFellow Part 1
  2. linear algebra：
    • 《Mathematics for Machine Learning: Linear Algebra》
  3. Probability Theory and Mathematical Statistics：
    • "Probability Theory and Mathematical Statistics" Zhejiang University Higher Education Press

DAY5-7: Python and commonly used libraries

1. Specific requirements

  1. Python common syntax：
    python-basicopen in new window
    • control statement
    • function
    • class
  2. Python commonly used libraries：

Read the official learning documents and matchData-Science-Notesopen in new windowWarehouse part learning

2. Display of learning results

Select some knowledge points for targeted exercises:

  1. Python exercises：
  2. Numpy Pandas and other exercises：

3. Reference materials

  1. Python and common libraries