Numpy与矩阵教学（线性代数） - Linear Algebra Learning

Example

Using `np.array` to create arrays and `reshape` to alter dimensions.

Scenario

In data science, reshaping data arrays to fit the requirements of machine learning models.

Example

Applying `np.dot` for matrix multiplication and `np.linalg.inv` for finding the inverse of a matrix.

Scenario

In engineering, to solve systems of linear equations or to understand the relationships between variables.

Example

Utilizing `np.linalg.solve` to find solutions to linear equations.

Scenario

In finance, for portfolio optimization and in economics for equilibrium analysis.

Example

Calculating determinants with `np.linalg.det` and inverses with `np.linalg.inv`.

Scenario

In physics, for understanding rotational dynamics or in cryptography for encoding and decoding messages.

Example

Finding eigenvalues and eigenvectors using `np.linalg.eig`.

Scenario

In machine learning, for principal component analysis (PCA) to reduce dimensionality of datasets.

Example

Applying `np.linalg.svd` for decomposing a matrix.

Scenario

In image processing, for image compression and noise reduction.