execut pip install -r requirements.txt

1. Using the softmax implementation method in our class, write your implementation in q1_softmax.py. You may test by executing python q1_softmax.py

Notes: The : The provided tests are not exhaustive. Later parts of the assignment will reference this code so it is important to have a correct implementation. Your implementation should also be efficient and vectorized whenever possible (i.e., use numpy matrix operations rather than for loops).

1. Derive the gradients of sigmoid function. Recall: the sigmoid function is $ \sigma = \frac{1}{1 + e^{-x}} $