30 Steps · Instant PDF Access · Fine-Tuning, Decided Properly
The Network Room

Transfer

You don't have a million images. You don't need them.

Thirty steps on starting from a network that already learned: what the early layers know that is worth keeping, which ones to freeze, how small your dataset can honestly be, and how to tell adaptation from memorisation.

$10.00One-time
Borrow A Network That Already LearnedInstant PDF · 30 worked entries
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See what's inside ↓

Thirty steps on what to freeze, what to replace, and how little data you need.
Borrow A Network That Already LearnedFrom the book
A 41-page PDF built around one decision tableWhat you get
30 stepsEach a stated decision
5 chaptersChoose, adapt, verify
Instant accessEmailed the moment you buy
Any devicePhone, tablet, laptop or print

Thirty steps across five chapters — what is transferable, the freezing decision, the new head, the fine-tuning schedule and the honest evaluation.

  • A decision table for freezing, from dataset size and domain distance
  • Head replacement with the shapes worked out, not guessed
  • A learning-rate schedule that protects what was already learned
  • The evaluation that separates adaptation from memorisation

Sound familiar?

Four hundred images, and every tutorial assumes a million.

So you train from scratch, it overfits by epoch two, and the conclusion you draw is that you need more data. Sometimes that is true. Far more often you needed a different starting point, because the early layers of somebody else's network already learned the things your four hundred images cannot teach.

Transfer learning is not a trick. It is a decision with three or four settings — what to freeze, what to replace, how fast to move — and each one has a reason you can state.

Sample pages

A look inside

Each step names the decision, what it depends on, what it costs, and how to tell whether it worked.

Step 05 · What is worth keeping

What Early Layers Actually Learned

  • A pretrained stack
  • A feature map
  • Your own images
Time 30 minLook before you freeze

Step 11 · The main decision

Freeze, Partially Freeze, Or Not

  • Dataset size
  • Domain distance
  • A decision table
Time 25 minSmaller set, more frozen

Step 22 · The honest check

Adapting Or Memorising?

  • A held-out set
  • The training curve
  • One comparison
Time 30 minCheck before you believe it

30 steps in total

A borrowed network carries the biases of the data it was trained on into your problem. The book is explicit about that before it is about accuracy.

What's inside

Here's exactly what you'll be able to do

Why this works

It turns fine-tuning from a recipe you copy into four decisions you make, each with a stated reason and a way to check it.

Decisions, Not Recipes

What to freeze depends on two things you can measure. The book gives you the table and the reasoning behind it.

Honest About Small Data

There is a size below which a result stops being evidence. The book says where that is and how to tell you are under it.

Checks Built In

Every decision comes with the comparison that tells you whether it helped, rather than a number you hope is good.

What you get

Everything in this book, listed

A 41-page PDF built around one decision tableWhat you get
30 stepsEach a stated decision
5 chaptersChoose, adapt, verify
Instant accessEmailed the moment you buy
Any devicePhone, tablet, laptop or print

Thirty steps across five chapters — what is transferable, the freezing decision, the new head, the fine-tuning schedule and the honest evaluation.

  • A decision table for freezing, from dataset size and domain distance
  • Head replacement with the shapes worked out, not guessed
  • A learning-rate schedule that protects what was already learned
  • The evaluation that separates adaptation from memorisation

The offer

Start from what somebody else already learned.

Borrow A Network That Already Learned is thirty steps on transfer learning — what to freeze, what to replace, how fast to move, and how to tell whether the result means anything.

Yes, show me how to borrow — Only $10.00

Instant digital download. One-time payment of $10.00. Educational material about how neural networks work — not a course, not a certification, and not a promise about employment or salary.

The 60-day guaranteeRead it, run one of the worked examples, and if it does not make the inside of a network clearer than the tutorial you abandoned last week, email us within 60 days for a full refund. No questions, no hard feelings.

Questions

Before you buy

How small can my dataset be?

The book answers this with ranges and the reasoning behind them, and it is honest about the point below which a result stops being evidence rather than becoming a worse result.

Does this cover fine-tuning language models?

The decisions are the same and the book names where they differ. The worked examples are image models because they are small enough to run while you read.

Where do pretrained models come from?

The book explains how to read a model card and what to check before you build on somebody else's weights — including licence and training data.

Do I need the convolution book?

No, but it helps for the chapter about which early layers are worth keeping.

Will fine-tuning always beat training from scratch?

No, and one chapter is about recognising when it will not — mainly when your domain is far from the source.

Is this a physical book?

No — it's a digital guide (PDF), emailed to you right after purchase. Nothing is shipped.

What if it isn't for me?

You're protected by a 60-day money-back guarantee. Email us within 60 days for a full refund — no questions asked.

You don't need a million images

Thirty steps on what to freeze, what to replace, and how to check the result is real.

$10.00One-time
Borrow A Network That Already LearnedInstant PDF · 30 worked entries
Yes, show me how to borrow — Only $10.00

Secure checkout · Instant download · 60-day guarantee

This is educational material about how neural networks work. It is not a course, a certification, a bootcamp or a career programme, and it makes no promise about employment, salary or professional outcomes of any kind. The code and the worked figures are teaching examples, written for clarity rather than for production: read them, adapt them, and test anything you reuse. Library APIs change often, so the method is what carries over, not the exact call signature. Nothing here is professional, legal or financial advice. Models reproduce the patterns and the biases of the data they are trained on; deploying one that affects people carries responsibilities this book does not cover.