6 Deep Learning and Transformer Basics

Chapter 6 has one job: help you understand how a model learns from loss, gradients, and repeated training steps.

See The Training Loop

Read the picture first. Most deep learning training code is this loop:

batch data -> model forward -> loss -> backward gradients -> optimizer step -> curves

Do not start by chasing large models. First make a small model train, log what happened, and explain why it improved or failed.

Learning Order And Task List

Use this table as both the chapter guide and the task sheet.

Page	Follow-along action	Evidence to keep
6.1 Neural Network Basics	Understand neurons, activations, forward/backward pass, optimizers, regularization, and initialization	One hand-written training-loop explanation
6.2 PyTorch	Practice tensors, autograd, nn.Module, Dataset, DataLoader, and a minimal training loop	One runnable PyTorch script
6.3 CNN	Use image classification to connect data shape, convolution, pooling, and transfer learning	Shape notes and one image-classification run
6.4 RNN	Learn why sequence data needs memory and how LSTM/GRU helped before Transformer	One sequence-model note
6.5 Transformer	Learn Query, Key, Value, self-attention, positional encoding, and Transformer blocks	One attention input/output diagram
6.1.8 Optional DL History	Skim why backprop, CNN, RNN, Attention, and Transformer appeared after you know the main loop	A short “why this architecture exists” note
6.6 Generative Models and 6.7 Training Tips	Treat as extensions after the training loop is stable	One tuning or diagnosis note
6.8 Projects and 6.8.5 Workshop	Build a PyTorch evidence pack before larger image, sentiment, or generative projects	Logs, curves, checkpoint, shape trace, README

Key terms for this chapter:

Term	Meaning
tensor	Multi-dimensional array used by PyTorch
forward	Data passes through the model to produce predictions
loss	Number that measures prediction error
backward	Computes gradients from the loss
optimizer	Updates parameters using gradients
epoch	One pass through the training data
batch	A small group of samples processed together

First Runnable Loop

Install PyTorch from the official selector if needed, then run this tiny loop after PyTorch is available:

import torch
from torch import nn

torch.manual_seed(42)
x = torch.tensor([[0.0], [1.0], [2.0], [3.0]])
y = torch.tensor([[0.0], [2.0], [4.0], [6.0]])

model = nn.Linear(1, 1)
loss_fn = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

for epoch in range(20):
    pred = model(x)
    loss = loss_fn(pred, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    if epoch in {0, 1, 5, 19}:
        print(epoch, round(loss.item(), 4))

Expected shape:

0 ...
1 ...
5 ...
19 ...

The exact numbers can differ, but the loss should generally move down. If it does, you have seen the training loop work.

Depth Ladder

Level	What you can prove
Minimum pass	You can describe forward, loss, backward, and optimizer step in order.
Project-ready	You can run a small PyTorch model, watch loss change, and interpret tensor shapes.
Deeper check	You can overfit one tiny batch on purpose, then explain why that test is useful before training a bigger model.

Common Failures

Symptom	First thing to check	Usual fix
Shape mismatch	Input shape, batch dimension, output classes	Print tensor shapes at each layer
Loss does not decrease	Learning rate, labels, normalization, loss function	Try overfitting one small batch first
Train good, validation poor	Overfitting or bad split	Add validation curve, augmentation, regularization, early stopping
Out of memory	Batch size, image size, model size	Reduce batch/resolution or use a smaller model
Transformer feels abstract	Q/K/V and sequence length	Draw one attention table before code

Pass Check

Move to Chapter 7 when you can answer these five questions:

• What happens in forward, loss.backward(), and optimizer.step()?
• What problem do Dataset and DataLoader solve?
• How do training and validation curves reveal overfitting?
• Why can Attention model context?
• How does Transformer connect to later large models?

For a printable checklist, use 6.0 Study Guide and Task Sheet. Later LLMs, RAG, and multimodal models all build on these representation-learning ideas.