Week 3 · Read arrays and explain them with plots

1. Create a NumPy Array from $\mathrm{score}=\begin{bmatrix}72&85&90&68&95&88\end{bmatrix}$ and print its shape, dtype, mean, values at least 80, and their count.
2. Arrange 1~20 into (4,5), then print the first row, last column, middle 2-row×3-column region, and even values separately.

1. For $A=\begin{bmatrix}2.0&1.0\\1.0&3.0\end{bmatrix}$ and $B=\begin{bmatrix}1.0&0.0\\2.0&1.0\end{bmatrix}$, compute the Element-wise Product and Matrix Multiplication and explain how element (0,1) is obtained.
2. Compute A's Determinant and Inverse and use np.allclose to verify that A@Inverse is the Identity.
3. Change A[0,0] to 4 and repeat.

1. Create x with 100 samples from 0~2π and plot sin and cos together.
2. Then use $\mathrm{epoch}=\begin{bmatrix}1&2&3&4&5\end{bmatrix}$, $\mathrm{loss}=\begin{bmatrix}1.8&1.2&0.9&0.7&0.6\end{bmatrix}$, and $\mathrm{accuracy}=\begin{bmatrix}0.4&0.6&0.7&0.75&0.78\end{bmatrix}$ to display sin, cos, Loss, and Accuracy in a 2×2 Figure.
3. Label axes and add a Legend, locate the maximum Accuracy in code, and mark it with an annotation.

Include code, actual outputs and plots, and your own interpretation. Submit through KLAS; check the semester schedule and latest announcements for the deadline.

Filename: Name_StudentID_Week3.ipynb.

reshape with an incompatible element count

1. After importing NumPy, add a Code Cell at the bottom of the notebook and run the following code.

   debug_values = np.arange(12)
   for debug_shape in [(5, 3), (3, 4)]:
       print("requested shape:", debug_shape, "elements:", debug_values.size)
       try:
           print(debug_values.reshape(debug_shape))
       except ValueError as error:
           print(type(error).__name__, str(error))

2. In a Text Cell, tabulate the required element count and execution success for each requested shape. Also explain in one sentence the order in which values from 0 to 11 are arranged in the successful array.