Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

# Sanity Check: do we get the same results as our
# gradient descent?
linr = LinearRegression()
linr.fit(x_train, y_train)
print(linr.intercept_, linr.coef_[0])
Output
# intercept and coef from Scikit-Learn
[1.02354075] [1.96896447]
They match up to six decimal places—we have a fully working implementation of
linear regression using Numpy.
Time to TORCH it!
PyTorch
First, we need to cover a few basic concepts that may throw you off-balance if you
don’t grasp them well enough before going full-force on modeling.
In deep learning, we see tensors everywhere. Well, Google’s framework is called
TensorFlow for a reason! What is a tensor, anyway?
Tensor
In Numpy, you may have an array that has three dimensions, right? That is,
technically speaking, a tensor.
A scalar (a single number) has zero dimensions, a vector has one
dimension, a matrix has two dimensions, and a tensor has three
or more dimensions. That’s it!
But, to keep things simple, it is commonplace to call vectors and matrices tensors as
well—so, from now on, everything is either a scalar or a tensor.
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