42 Training Failures · Instant PDF Access · Diagnose, Then Fix

Training

The loss went to NaN at epoch three. It was not random.

Forty-two ways training fails, and what each one looks like before it fails: the NaN, the curve that never moves, the one that falls then explodes, and the quiet one where everything looks fine and the model learned nothing useful.

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The Loss Went To NaNInstant PDF · 42 worked entries

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Forty-two training failures, each with the curve, the cause and the fix. — The Loss Went To NaNFrom the book

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42 worked entries
6 chapters
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A 54-page PDF organised by the symptom you're staring atWhat you get

42 failuresEach with its curve

6 chaptersGrouped by symptom

Instant accessEmailed the moment you buy

Any devicePhone, tablet, laptop or print

Forty-two failures across six chapters — NaNs, flat losses, instability, overfitting, data faults and the silent ones.

Loss curves drawn for each failure so you can match what you are looking at
Causes ordered by probability, with the one measurement that confirms each
A chapter on runs that look successful and are not
A pre-flight checklist that catches most failures in the first fifty steps

Sound familiar?

You lowered the learning rate. It worked. You don't know why.

Training failures all look the same from the outside — a number that misbehaves — so the response becomes ritual. Lower the learning rate, add a normalisation layer, try again, and hope. Sometimes it works, which is worse, because it teaches you nothing.

Each failure has a shape. A NaN from an exploding gradient looks different from a NaN from a log of zero. A flat loss from a dead activation looks different from a flat loss from a label that never changes. Once you can tell them apart, the fix follows.

Sample pages

A look inside

Each failure shows the curve, what it tells you, the likely causes in order of probability, and how to confirm which one it is before changing anything.

Failure 03 · The classic NaN

Log Of Zero In The Loss

A softmax output
A log
One epsilon

Time 15 minCheck the loss, not the weights

Failure 17 · The flat line

Dead Units, And How To See Them

Activation statistics
One histogram
The initialisation

Time 30 minLog the fraction at zero

Failure 29 · The quiet one

It Learned The Majority Class

An imbalanced set
Accuracy that looks fine
A confusion matrix

Time 25 minNever trust accuracy alone

42 training failures in total

A model that scores well and learned the wrong thing is the most expensive failure here. Those get their own chapter, with the checks that reveal them.

What's inside

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

Tell an exploding-gradient NaN from a log-of-zero NaN by looking at where it appears
Read a loss curve and name the three most likely causes before touching the code
Confirm a diagnosis with one measurement instead of changing four things at once
Spot dead activations with a histogram, and fix the initialisation that caused them
Separate overfitting from a broken validation split, which look identical at first
Detect a model that learned the majority class while reporting good accuracy
Build a short pre-flight check that catches most of these before a long run

Why this works

It is organised by symptom, not by technique — you arrive with a curve that looks wrong and leave knowing which measurement settles it.

Diagnose Before Fixing

Every entry names the confirming measurement first. Changing the learning rate to see what happens is explicitly the thing this book is against.

Ordered By Probability

Causes are listed most-likely first, so you check the cheap common one before the exotic one.

Includes The Silent Failures

A whole chapter on runs that complete, report good numbers, and are worthless — because those cost weeks rather than hours.

What you get

Everything in this book, listed

A 54-page PDF organised by the symptom you're staring atWhat you get

42 failuresEach with its curve

6 chaptersGrouped by symptom

Instant accessEmailed the moment you buy

Any devicePhone, tablet, laptop or print

Forty-two failures across six chapters — NaNs, flat losses, instability, overfitting, data faults and the silent ones.

Loss curves drawn for each failure so you can match what you are looking at
Causes ordered by probability, with the one measurement that confirms each
A chapter on runs that look successful and are not
A pre-flight checklist that catches most failures in the first fifty steps

The offer

Diagnose it. Then fix it.

The Loss Went To NaN is forty-two training failures organised by symptom — the curve, what it rules out, the likely causes in order, and the measurement that confirms which one you have.

Yes, help me diagnose it — 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

Is this just 'lower the learning rate' forty-two times?

No — and the book argues against that reflex explicitly. Every entry names a measurement that distinguishes its cause from the others before any change is suggested.

Does it cover hyperparameter tuning?

Only where tuning is the actual fix. Most of these failures are not tuning problems, which is why tuning them feels like luck.

Which framework?

The diagnoses are framework-independent; the measurement snippets are PyTorch, with NumPy equivalents where it helps.

Do I need the other books?

No. Where a failure needs a gradient or a shape explained, it is explained in place.

Will this make my model accurate?

It will stop it failing for reasons you cannot name. Whether a model reaches useful accuracy depends on the data and the problem, and no book can promise that.

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.

Forty-two failures, already diagnosed

Match the curve, read the likely causes, take the one measurement that settles it.

$10.00One-time

The Loss Went To NaNInstant PDF · 42 worked entries

Yes, help me diagnose it — 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.