PyTorch Tutorial for Beginners — Where to Begin in 2026

Beginner PyTorch:
"First tensor"
import torch
x = torch.tensor([1.0, 2.0, 3.0])
print(x.mean())

What this gets you:
- A working tensor
- The ability to run code on GPU
- The foundation for all deep learning

What it signals:
- You can work with data in PyTorch
- You understand the core structure
- You are ready to go deeper

Intermediate PyTorch:
"Gradient computation"
import torch
x = torch.tensor(2.0, requires_grad=True)
y = x ** 2 + 3 * x
y.backward()
print(x.grad)  # 7.0

What this gets you:
- Automatic differentiation
- The core of neural network training
- The skill every ML engineer needs

What it signals:
- You understand backpropagation
- You can train models
- You are ready for a real project

Tensors:
"Operations on arrays"
import torch
a = torch.tensor([1, 2, 3])
b = torch.tensor([4, 5, 6])
print(a + b)
print(a * b)
print(a.sum())

What this gets you:
- Fast numerical operations
- GPU support
- The foundation for models

What it signals:
- You can work with numerical data
- You understand shapes and types
- You are ready for autograd

Training Loop:
"Forward, loss, backward, step"
for epoch in range(10):
    pred = model(x)
    loss = loss_fn(pred, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

What this gets you:
- A trained model
- The core of all deep learning
- The workflow used by every ML team

What it signals:
- You understand the training process
- You can debug model behavior
- You are ready for a real project

import torch
import torch.nn as nn

# data
x = torch.randn(100, 1)
y = 3 * x + 2 + 0.1 * torch.randn(100, 1)

# model
model = nn.Linear(1, 1)
loss_fn = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

# training loop
for epoch in range(100):
    pred = model(x)
    loss = loss_fn(pred, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print(model.weight.item(), model.bias.item())

Line-by-line:
x, y: synthetic data (y = 3x + 2 + noise)

nn.Linear(1, 1): one input, one output
→ the simplest possible model

nn.MSELoss(): mean squared error
→ the standard loss for regression

torch.optim.SGD: stochastic gradient descent
→ the simplest optimizer

for epoch in range(100):
→ iterate over the data 100 times

pred = model(x): forward pass
loss = loss_fn(pred, y): compute error
optimizer.zero_grad(): clear old gradients
loss.backward(): compute new gradients
optimizer.step(): update the weights

After 100 epochs:
model.weight ≈ 3
model.bias ≈ 2

That is how you learn the pattern.

Weak Answer:
"I know PyTorch and trained a model."

Interviewer: "Cool. Tell me more."
You: "I used nn.Module and an optimizer."

Interviewer: "Okay... how did it perform?"
You: "Not sure."

The conversation ends here.

Strong Answer:
"I built a small image classifier in PyTorch
for a side project. My first version had poor
recall on rare classes — the model predicted
the majority class almost every time.

I checked the class distribution, and it was
severely imbalanced. I added class weights to
the loss function and switched to focal loss,
which focuses on hard examples.

The result: recall on the rare class improved
from 34% to 71%, with only a small drop in
overall accuracy. That taught me that most ML
problems are data problems — the model is
just a tool."

The conversation continues.