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+# Matrix Transformation (Medium) ✔
+
+## Table of Contents
+
+- [Problem Statement](#problem-statement)
+- [Example](#example)
+- [Learn: Matrix Transformation](#learn-matrix-transformation)
+- [Solutions](#solutions)
+  - [Implementation](#implementation)
+- [Code Explanation](#code-explanation)
+
+## Problem Statement
+
+[Matrix Transformation](https://www.deep-ml.com/problem/Matrix%20Transformation)
+
+Write a Python function that transforms a given matrix A using the operation $$T^{-1}AS$$, where T and S are invertible matrices. The function should first validate if the matrices T and S are invertible, and then perform the transformation.
+
+## Example
+
+```python
+input: A = [[1, 2], [3, 4]], T = [[2, 0], [0, 2]], S = [[1, 1], [0, 1]]
+output: [[0.5,1.5],[1.5,3.5]]
+reasoning: The matrices T and S are used to transform matrix A by computing T^(-1)AS.
+```
+
+## Learn: Matrix Transformation
+
+Transforming a matrix $$A$$ using the operation $$T^{-1}AS$$ involves several steps. This operation changes the basis of matrix $$A$$ using two invertible matrices $$T$$ and $$S$$. Given matrices $$A$$, $$T$$, and $$S$$:
+
+1. **Check if $$T$$ and $$S$$ are invertible** by ensuring their determinants are non-zero.
+2. **Compute the inverses** of $$T$$ and $$S$$, denoted as $$T^{-1}$$ and $$S^{-1}$$.
+3. **Perform the matrix multiplication** to obtain the transformed matrix:
+
+   $$A' = T^{-1}AS$$
+
+### Example
+
+If:
+
+$$
+A = \begin{pmatrix} 
+1 & 2 \\ 
+3 & 4 
+\end{pmatrix}
+$$
+
+$$
+T = \begin{pmatrix} 
+2 & 0 \\ 
+0 & 2 
+\end{pmatrix}
+$$
+
+$$
+S = \begin{pmatrix} 
+1 & 1 \\ 
+0 & 1 
+\end{pmatrix}
+$$
+
+First, check that $$T$$ and $$S$$ are invertible:
+- $$det(T) = 4 \neq 0$$
+- $$det(S) = 1 \neq 0$$
+
+Compute the inverses:
+
+$$
+T^{-1} = \begin{pmatrix} 
+\frac{1}{2} & 0 \\ 
+0 & \frac{1}{2} 
+\end{pmatrix}
+$$
+
+Then, perform the transformation:
+
+$$
+A' = T^{-1}AS = \begin{pmatrix} 
+\frac{1}{2} & 0 \\ 
+0 & \frac{1}{2} 
+\end{pmatrix} \begin{pmatrix} 
+1 & 2 \\ 
+3 & 4 
+\end{pmatrix} \begin{pmatrix} 
+1 & 1 \\ 
+0 & 1 
+\end{pmatrix} = \begin{pmatrix} 
+0.5 & 1.5 \\ 
+1.5 & 3.5 
+\end{pmatrix}
+$$
+
+## Solutions
+
+### Implementation
+
+```python
+import numpy as np
+
+def transform_matrix(
+    A: list[list[int | float]], T: list[list[int | float]], S: list[list[int | float]]
+) -> list[list[int | float]]:
+
+    if np.linalg.det(T) == 0 or np.linalg.det(S) == 0:
+        raise ValueError(
+            "The determinant/s of the matrice/s equals 0; hence not invertible"
+        )
+    transformed_matrix = np.linalg.inv(T) @ A @ S
+    return transformed_matrix  # type: ignore
+
+A = [[1, 2], [3, 4]]
+T = [[2, 0], [0, 2]]
+S = [[1, 1], [0, 1]]
+print(transform_matrix(A=A, T=T, S=S))  # type: ignore
+```
+
+## Code Explanation
+
+The `transform_matrix` function takes three parameters:
+- `A`: The matrix to be transformed (list of lists)
+- `T`: The first transformation matrix (list of lists)
+- `S`: The second transformation matrix (list of lists)
+
+The function implements the matrix transformation as follows:
+
+1. It first checks if matrices `T` and `S` are invertible by calculating their determinants using `np.linalg.det()`. If either determinant is zero, it raises a `ValueError` with an appropriate message.
+
+2. If both matrices are invertible, it performs the transformation using NumPy's matrix operations:
+   - `np.linalg.inv(T)` computes the inverse of matrix `T`
+   - The `@` operator is used for matrix multiplication
+   - The operation `np.linalg.inv(T) @ A @ S` performs the transformation $$T^{-1}AS$$
+
+3. The function returns the transformed matrix.
+
+Note: The `# type: ignore` comments are used to suppress type checking warnings. In this case, they're used because the return type of NumPy operations might not strictly match the type hint `list[list[int | float]]`.
+
+The commented out "Step 2" section is identical to the implemented "Step 1" section. It appears to be a duplicate of the same function, possibly left as a backup or for comparison purposes.
+
+In the example usage, matrices `A`, `T`, and `S` are defined, and the `transform_matrix` function is called with these matrices as arguments. The result is then printed.
+
+This implementation efficiently uses NumPy for matrix operations, which is particularly beneficial for larger matrices due to NumPy's optimized array operations.