Learn all the basics you need to get started with this deep learning framework! In this part we implement a logistic regression algorithm and apply all the concepts that we have learned so far:

- Training Pipeline in PyTorch
- Model Design
- Loss and Optimizer
- Automatic Training steps with forward pass, backward pass, and weight updates

All code from this course can be found on GitHub.

## Linear Regression in PyTorch

```
import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
# 0) Prepare data
X_numpy, y_numpy = datasets.make_regression(n_samples=100, n_features=1, noise=20, random_state=4)
# cast to float Tensor
X = torch.from_numpy(X_numpy.astype(np.float32))
y = torch.from_numpy(y_numpy.astype(np.float32))
y = y.view(y.shape[0], 1)
n_samples, n_features = X.shape
# 1) Model
# Linear model f = wx + b
input_size = n_features
output_size = 1
model = nn.Linear(input_size, output_size)
# 2) Loss and optimizer
learning_rate = 0.01
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# 3) Training loop
num_epochs = 100
for epoch in range(num_epochs):
# Forward pass and loss
y_predicted = model(X)
loss = criterion(y_predicted, y)
# Backward pass and update
loss.backward()
optimizer.step()
# zero grad before new step
optimizer.zero_grad()
if (epoch+1) % 10 == 0:
print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')
# Plot
predicted = model(X).detach().numpy()
plt.plot(X_numpy, y_numpy, 'ro')
plt.plot(X_numpy, predicted, 'b')
plt.show()
```