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

Step 0 - Random Initialization
For training a model, you need to randomly initialize the parameters / weights (we
have only two, b and w).
Step 0
# Step 0 - Initializes parameters "b" and "w" randomly
np.random.seed(42)
b = np.random.randn(1)
w = np.random.randn(1)
print(b, w)
Output
[0.49671415] [-0.1382643]
Step 1 - Compute Model’s Predictions
This is the forward pass; it simply computes the model’s predictions using the current
values of the parameters / weights. At the very beginning, we will be producing really
bad predictions, as we started with random values from Step 0.
Step 1
# Step 1 - Computes our model's predicted output - forward pass
yhat = b + w * x_train
Step 2 - Compute the Loss
For a regression problem, the loss is given by the mean squared error (MSE); that
is, the average of all squared errors; that is, the average of all squared differences
between labels (y) and predictions (b + wx).
In the code below, we are using all data points of the training set to compute the
loss, so n = N = 80, meaning we are performing batch gradient descent.
64 | Chapter 1: A Simple Regression Problem