The document discusses NumPy, a package for scientific computing in Python. It covers key NumPy functions like np.exp and np.reshape, and concepts like broadcasting. The document also discusses using NumPy for machine learning tasks like the sigmoid function and its gradient, as well as reshaping arrays and images. Common NumPy operations in deep learning are discussed, such as reshaping, shape, and broadcasting.

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Demo1: Use Numpy.

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Be able to use numpy functions and numpy matrix/vector operations
Understand the concept of "broadcasting"
Be able to vectorize code
Demo 1: Use Numpy
Objecifs???

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Numpy is the main package for scientific computing in Python.
Demo 1: Use Numpy
What is numpy???

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It is maintained by a large community (www.numpy.org).
Demo 1: Use Numpy
Value of numpy???

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They are several key numpy functions such as:
np.exp, np.log, and np.reshape.
Demo 1: Use Numpy
How to use numpy???

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Demo 1: Use Numpy
sigmoid function, np.exp()

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It is a non-linear function used not only in Machine Learning (Logistic
Regression), but also in Deep Learning.
Demo 1: Use Numpy
We do use sigmoid in:

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It is a non-linear function used not only in Machine Learning (Logistic
Regression), but also in Deep Learning.
Demo 1: Use Numpy
Why use np.exp() not math.exp():

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you will need to compute gradients to optimize loss functions using back-
propagation.
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Sigmoid Gradient:

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Two common numpy functions used in deep learning are np.shape and
np.reshape().
X.shape is used to get the shape (dimension) of a matrix/vector X.
X.reshape(...) is used to reshape X into some other dimension.
Demo 1: Use Numpy
Reshaping Array:

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Demo 1: Use Numpy
Reshape an image:

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Demo 1: Use Numpy
Broadcasting and the softmax function:

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Demo 1: Use Numpy
Reshaping Array:
### START CODE HERE ### (≈ 3 lines of code)
x_exp = np.exp(x)
x_sum = np.sum(x_exp, axis = 1, keepdims = True )
s = x_exp/x_sum
### END CODE HERE ###

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Demo 1: Use Numpy
What you need to remember::
-np.exp(x) works for any np.array x and applies the
exponential function to every coordinate
-the sigmoid function and its gradient
-image2vector is commonly used in deep learning
-np.reshape is widely used. In the future, you'll see that
keeping your matrix/vector dimensions straight will go toward
eliminating a lot of bugs.
-numpy has efficient built-in functions
-broadcasting is extremely useful

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Question ???

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Merci pour votre aimable attention.