MLearning
/
Numpy
- 1 Supervised ML 4
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Classifier S
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Linear model S
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Basis expansion S
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Regularization S
- 2 Matplotlib 2
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Subplots S
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Pyplot S
- 3 Datasets 4
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Iris species S
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Diabetes S
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Breast cancer S
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Simulated data S
- 4 Numpy 7
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Matrices S
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Sparse matrices S
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Vectorize S
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Average S
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Standard deviation S
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Reshape S
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Multiplication S
- 5 Pandas 5
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Read data S
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Data cleaning S
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Find values S
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Group rows S
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Merge data S
- 6 Calculus 2
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Derivatives S
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Integrals S
- 7 Algorithms 3
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K nearest neighbors S
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Linear regression S
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Gradient descent S
S
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Q
ML Numpy Multiplication
Matrix multiplication, use @ Element-wise multiplication, we use * D = A @ B E = A * B
Addition
p17 For addition simply use + operator.
""" Addition and Substraction (+ -)
"""
import numpy as np
A = np.array([
[1, 1],
[2, 2],
])
B = np.array([
[1, 1],
[3, 3],
])
C = np.add(A, A) # first method
C = A + B # second method
print(C)
# [2 2]
# [5 5]
assert (np.add(A, A) == (A + A)) .all() # passed
➥ Multiplication
Multiplication
p17 For element-wise multiplication we use *
""" Matrix addition
For element-wise multiplication we use *
"""
import numpy as np
A = np.array([
[1, 1],
[2, 2],
])
B = np.array([
[1, 1],
[3, 3],
])
C = np.dot(A, B) # first method
C = A @ B # second method
E = A * B # element-wise multiplication
print(C)
# [4 4]
# [8 8]
print(E)
# [1 1]
# [6 6]
assert (np.dot(A, A) == (A @ A)) .all() # passed
assert (E[1, 1] == 6) # passed
➥ Questions
Last update: 47 days ago