https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/geometry-linear-algebraic-ops.html
Please review the section 17.1.8. Determinant.
The example matrix A is different from matrix A presented in 17.1.4. Geometry of Linear Transformations.
I think columns and rows need to exchanged in 17.1.8.
Hi @mathnow, thanks for your feedback! We have corrected it at https://github.com/d2l-ai/d2l-en/pull/722.
%matplotlib inline
import d2l
from IPython import display
from mxnet import gluon, np, npx
npx.set_np()
def angle(v, w):
return np.arccos(v.dot(w) / (np.linalg.norm(v) * np.linalg.norm(w)))
orthogonal_angle = angle(np.array([0, 1]), np.array([1, 0]))
orthogonal_angle.asscalar()
raise bug:
Hi @chibinjiang, both of the following methods can change it to scalar:
float(orthogonal_angle)
or
orthogonal_angle.item()
@gold_piggy Appreciate
But why does .asscalar() not work???
As you see, the orthogonal_angle has the attribute of asscalar() method.
There is not attribute of asscalar in np interface. Here are all the supported functions. Apache MXNet | A flexible and efficient library for deep learning.
Under Fig. 18.1.3, the vector 𝐮−𝐯 is the direction that takes us from the point 𝐯 to the point 𝐮, right?