Contenido del Curso
Mathematics for Data Analysis and Modeling
Mathematics for Data Analysis and Modeling
Challenge: Solving the Task Using SLE
Tarea
We have already considered how to solve the SLE using inversed matrix. But we can also use np.linalg.solve(A, y)
method that calculates the solution of the SLE:
A * x = y
.
Your task is to solve the system using both these methods and compare the results:
- Use
np.linalg.solve()
method. - Use
np.inv()
method to calculate inversed matrix and provide solution using:x = A_inv @ y
.
¡Gracias por tus comentarios!
Challenge: Solving the Task Using SLE
Tarea
We have already considered how to solve the SLE using inversed matrix. But we can also use np.linalg.solve(A, y)
method that calculates the solution of the SLE:
A * x = y
.
Your task is to solve the system using both these methods and compare the results:
- Use
np.linalg.solve()
method. - Use
np.inv()
method to calculate inversed matrix and provide solution using:x = A_inv @ y
.
¡Gracias por tus comentarios!
Challenge: Solving the Task Using SLE
Tarea
We have already considered how to solve the SLE using inversed matrix. But we can also use np.linalg.solve(A, y)
method that calculates the solution of the SLE:
A * x = y
.
Your task is to solve the system using both these methods and compare the results:
- Use
np.linalg.solve()
method. - Use
np.inv()
method to calculate inversed matrix and provide solution using:x = A_inv @ y
.
¡Gracias por tus comentarios!
Tarea
We have already considered how to solve the SLE using inversed matrix. But we can also use np.linalg.solve(A, y)
method that calculates the solution of the SLE:
A * x = y
.
Your task is to solve the system using both these methods and compare the results:
- Use
np.linalg.solve()
method. - Use
np.inv()
method to calculate inversed matrix and provide solution using:x = A_inv @ y
.