## How to Build an AI Model From Scratch Using PyTorch

This is not a “build AI in 10 minutes” post.
This is not a copy-paste tutorial.
This is how you actually build an AI model — slowly, clearly, and without lying to yourself.

If you searched “how to build an AI model from scratch”, you’re in the right place.

### Table of Contents

1. What Does “From Scratch” Mean in AI?
2. What Is an AI Model? (Simple Explanation)
3. Installing PyTorch
4. Understanding Tensors in PyTorch
5. Gradients and Autograd
6. What Is a Neural Network?
7. Loss Functions Explained
8. Optimizers Explained
9. The Training Loop
10. Why AI Models Start Bad
11. Evaluating a Model Properly
12. Saving and Loading a PyTorch Model
13. Common Beginner Mistakes
14. What to Learn Next
15. Final Thoughts

#### Technical Requirements & Setup (2026)

To follow this guide and begin building, ensure your environment meets these specifications:

### What Does “From Scratch” Mean in AI?

Let’s be honest.

When most people say “from scratch”, they mean:

• imported a pre-trained model
• followed a tutorial blindly
• hoped it works
• closed the tab when it didn’t

That’s not what we’re doing.

#### From scratch means:

• You write the neural network yourself
• You understand tensors and gradients
• You control the training loop
• You know why the model learns
• You can debug when it breaks (it will)

We will use PyTorch, but only as a tool, not a brain replacement.

### What Is an AI Model? (Simple Explanation)

An AI model is not:

• intelligent
• conscious
• thinking
• alive

An AI model is:

• a bunch of numbers
• doing math
• trying to be less wrong over time

That’s it.

#### The Core Idea of Machine Learning

Every AI model ever made follows this loop:

Guess → Measure error → Adjust numbers → Repeat

That loop is learning.

### Installing PyTorch

Install PyTorch:

pip install torch torchvision torchaudio

Verify installation:

import torch

print(torch.__version__)
print(torch.cuda.is_available())

• True → GPU acceleration 🚀
• False → CPU training (still works)

### Understanding Tensors in PyTorch

Everything in PyTorch is a tensor.

• 0D → number
• 1D → list
• 2D → table
• 3D → image
• 4D → batch of images

Example:

import torch

x = torch.tensor([1.0, 2.0, 3.0])
print(x)

Simple. Powerful. Dangerous.

### Gradients and Autograd

A gradient answers one question:

“If I change this number slightly, does the result get better or worse?”

Example:

import torch

x = torch.tensor(2.0, requires_grad=True)
y = x ** 2
y.backward()

print(x.grad)

Output:

4

PyTorch handles gradients using autograd.
You don’t do calculus. PyTorch does.

### What Is a Neural Network?

A neural network is just:

• input numbers
• multiplied by weights
• passed through functions
• repeated a few times

That’s literally it.

#### Creating a Neural Network in PyTorch

import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(10, 64)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(64, 1)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

Human translation:

• Take 10 numbers
• Turn them into 64 features
• Remove negative vibes (ReLU)
• Output 1 number

You just built a neural network.

### Loss Functions Explained

The model predicts.
The loss function replies:

“Bro… that’s wrong.”

Example:

loss_fn = nn.MSELoss()

Mean Squared Error punishes big mistakes hard — which helps learning.

### Optimizers Explained

The optimizer updates the model’s weights.

optimizer = torch.optim.Adam(
    model.parameters(),
    lr=0.001
)

Adam is popular because:

• stable
• fast
• beginner-friendly

### The Training Loop

This is where AI is actually created.

for epoch in range(100):
    optimizer.zero_grad()

    predictions = model(inputs)
    loss = loss_fn(predictions, targets)

    loss.backward()
    optimizer.step()

    print(f"Epoch {epoch} | Loss: {loss.item()}")

What happens every epoch:

1. Clear old gradients
2. Predict
3. Measure error
4. Backpropagate blame
5. Update weights

Repeat.

### Why AI Models Start Bad

At the beginning:

• weights are random
• predictions are terrible
• loss is high

That’s normal.

Learning = controlled failure.

### Evaluating a Model Properly

model.eval()
with torch.no_grad():
    output = model(test_inputs)

Evaluation mode:

• disables randomness
• saves memory
• increases speed

### Saving and Loading a PyTorch Model

Save:

torch.save(model.state_dict(), "model.pt")

Load:

model.load_state_dict(torch.load("model.pt"))
model.eval()

Your AI survived.

### Common Beginner Mistakes

• Learning rate too high → NaN loss
• Forgetting zero_grad() → broken training
• No normalization → slow learning
• Training too long → overfitting
• Copy-paste learning → zero understanding

Mistakes are part of the process.

### What to Learn Next

• CNNs for images
• Transformers for text
• Custom datasets
• GPU training
• Writing your own layers
• Breaking models on purpose

### Final Thoughts

AI isn’t about being smart.
It’s about being stubborn enough to understand boring things deeply.

This website is for builders.
This is post #1.

Everything starts here.