How Should I Feed my Gradient Descent?

Photo by Jonathan Farber on Unsplash

INTRODUCTION

DATA

# Setting the seed
np.random.seed(0)

# Creating observations
X_train = np.random.rand(number_of_observations,number_of_features)
X_valid = np.random.rand(number_of_observations,number_of_features)

# Instantiating the parameters W and b
W = np.round(np.random.rand(number_of_features,1)*10, 0)
b = np.round(np.random.rand(1,1),0)

# Creating some noise
noise = np.random.randn(number_of_observations,1)

# Creating y by doing XW + b and some noise
y_train = np.dot(X_train, W) + b + noise
y_valid = np.dot(X_valid, W) + b + noise

# Printing coefficients
print(f"True W: {W[0][0]} \nTrue b: {b[0][0]}")

EXPERIMENT

CONCLUSION

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