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# Compose YML

In docker-compose file, we need to use bash -c to run multi commands.

We can use token to change password for jupyterlab on first login, then restart server.

# Edit kernel in JupyterLab

## Step 1 activate target environment ## Step 2 install ipykernel conda install ipykernel ## Step 3 set up kernel add kernel into jupyterlab python -m ipykernel --name [kernel name] ## Step 4 exsisting kernel jupyter kernelspec list ## Step 5 remove kernel jupyter kernelspec remove [kernel name]

# Syllabus

• White noise, PSD
• Filter

# Eigenvalue and Eigenvector

Eigenvectors and Eigenvalues are those vectors don’t change direction only stretching with the multiplication of eigenvalue. For a transformation that have eigenvectors can span the space, we can use eigenvectors as basis. If we do so, we can apply the eigenvectors to this transformation. The result must be a diagonal matrix, and the value equal to the eigenvalues. (eigenvector only stretch on its direction, so the value only shows on the diagonal) If we want to calculate 100-th power of this matrix, we can convert it to its system and calculate, then convert it back.

set of eigenvectors is also called eigenbasis.

Reference
https://www.3blue1brown.com/topics/linear-algebra

# Change of basis

The basis in Jennifer’s grid represent a transformation as matrix below. It can convert the vector in Jennifer’s language into the vector in our language. The inverse switches the direction. If we apply the inversion of basis matrix to the vector in our language, we can obtain the vector in Jennifer’s language. The function is inverse change of previous pic. This transformation can be written in A^(-1)MA. This represent convert other language into our language, then apply a transformation to it, finally convert our language to origin language. This transformation means a transformation on the other language.

Reference
https://www.3blue1brown.com/topics/linear-algebra