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11 - Practical Methodology

Advanced mastery of Chapter 11, Practical Methodology, covering every named section across book pages 421-442.

Learning path

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0 of 5 sections marked complete · about 59 minutes

Learning objectives

What you will be able to explain

  • Choose the performance metric
  • Compute precision
  • Compute recall
  • What makes a useful baseline?
  • Should you gather more data?
  • Manual hyperparameter search

Section 01

Choose the performance metric to Compute recall

01

Choose the performance metric

A metric must match the real task and error costs. A single aggregate can hide class imbalance, abstention, or asymmetric consequences.

Guided checkpoint

Match each requirement to the most relevant metric concept.

Source: Chapter 11, section 11.1, pp. 422-425

02

Compute precision

precision=TP/(TP+FP)=80/100=0.8precision=TP/(TP+FP)=80/100=0.8.

Guided checkpoint

A classifier produces 80 true positives and 20 false positives. Compute precision.

Source: Chapter 11, section 11.1, pp. 422-425

03

Compute recall

recall=TP/(TP+FN)=80/120=0.6666...recall=TP/(TP+FN)=80/120=0.6666....

Guided checkpoint

The same classifier has 80 true positives and 40 false negatives. Compute recall.

Source: Chapter 11, section 11.1, pp. 422-425

Section 02

What makes a useful baseline? to Manual hyperparameter search

01

What makes a useful baseline?

A baseline is a diagnostic anchor. Novelty before a reliable reference makes failures hard to localize.

Guided checkpoint

Select all recommended baseline properties.

Source: Chapter 11, section 11.2, pp. 425-426

02

Should you gather more data?

Learning curves and train-validation gaps help distinguish capacity, optimization, variance, and mismatch problems.

Guided checkpoint

Match the observed regime to the likely action.

Source: Chapter 11, section 11.3, pp. 426-427

03

Manual hyperparameter search

Manual search is strongest when guided by interpretable diagnostics and controlled experiments.

Guided checkpoint

Which sequence best uses diagnostic feedback?

Source: Chapter 11, section 11.4.1, pp. 427-431

Section 03

Why random search can beat a grid to Model-based hyperparameter optimization

01

Why random search can beat a grid

A grid allocates combinations uniformly across all dimensions; random trials cover each coordinate more richly when effective dimensionality is low.

Guided checkpoint

In a high-dimensional search where only two hyperparameters matter strongly, why is random search attractive?

Source: Chapter 11, section 11.4.2, pp. 431-433

02

Sampling hyperparameters on the right scale

Search spaces should reflect meaningful geometry and dependencies; a linear scale poorly resolves multiplicative uncertainty.

Guided checkpoint

Match the hyperparameter to a plausible search distribution.

Source: Chapter 11, section 11.4, pp. 427-436

03

Model-based hyperparameter optimization

Adaptive search can be sample-efficient but still consumes validation information and depends on surrogate assumptions.

Guided checkpoint

Judge each statement.

Source: Chapter 11, section 11.4.3, pp. 433-436

Section 04

High-value debugging strategies to The tiny-data test fails

01

High-value debugging strategies

Debugging requires falsifiable checks at data, objective, gradient, capacity, and implementation levels.

Guided checkpoint

Select all strategies recommended by the chapter.

Source: Chapter 11, section 11.5, pp. 436-440

02

Gradient-checking discipline

Truncation error dominates for large finite-difference steps and roundoff/cancellation dominates for excessively small steps.

Guided checkpoint

Evaluate each claim.

Source: Chapter 11, section 11.5, pp. 436-440

03

The tiny-data test fails

A sufficiently expressive model should memorize a tiny sample. Failure localizes the issue upstream of generalization.

Guided checkpoint

A large network cannot reduce loss on one fixed minibatch. What should you suspect first?

Source: Chapter 11, section 11.5, pp. 436-440

Section 05

Multi-digit recognition case study

01

Multi-digit recognition case study

The example illustrates decomposing a practical system while keeping evaluation aligned with the final structured task.

Guided checkpoint

Match each system concern to its function.

Source: Chapter 11, section 11.6, pp. 440-442

Knowledge check

Turn understanding into recall.

The quiz now follows the same concepts in scored form. You can return to this lesson from the quiz whenever a gap appears.

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