Your First Neural Network in PyTorch

PyTorch is the industry standard for deep learning. Here is a complete network — every line explained. This template scales from toy problems to research.

Tensors — NumPy arrays that run on GPUs and track gradients

import torch
x = torch.tensor([1.0, 2.0, 3.0])
x * 2                         # like NumPy
x.to("cuda")                  # move to GPU — this is the superpower

Define the network

import torch.nn as nn

model = nn.Sequential(
    nn.Linear(4, 16),   # 4 inputs -> 16 hidden units
    nn.ReLU(),          # non-linearity
    nn.Linear(16, 3),   # 16 -> 3 output classes
)

The training loop — the heart of deep learning

loss_fn   = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

for epoch in range(100):
    optimizer.zero_grad()          # 1. reset gradients
    preds = model(X_train)         # 2. forward pass (predict)
    loss  = loss_fn(preds, y_train)# 3. measure error
    loss.backward()                # 4. backprop (compute gradients)
    optimizer.step()               # 5. update weights (step downhill)

Those five lines are every deep learning model, from this toy to GPT. zero_grad → forward → loss → backward → step. Memorise the rhythm.

Run it free with a GPU

Paste into Google Colab, set Runtime → GPU, and you are training on hardware that cost thousands, for free. Next: CNNs for images.

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Neural Networks From Scratch — The Intuition

CNNs Explained — How Computers See Images