Learning rate selection in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the learning rate in machine learning?

The learning rate is a number that controls how much the model changes its knowledge after seeing new data. It decides the size of steps the model takes to learn.

beginner

What happens if the learning rate is too high?

If the learning rate is too high, the model might jump around and never settle on a good solution. It can miss the best answer and perform poorly.

beginner

Why is a very low learning rate not always good?

A very low learning rate makes the model learn very slowly. It takes a long time to improve and might get stuck before finding the best solution.

intermediate

What is a learning rate schedule?

A learning rate schedule changes the learning rate during training. It can start high to learn fast, then lower to fine-tune the model carefully.

intermediate

How can you find a good learning rate?

You can try different learning rates and watch how the model learns. Tools like learning rate range tests help find a rate that makes the model improve steadily.

What does a learning rate control in training a model?

A The amount of data used for training

B The number of layers in the model

C The size of steps the model takes to learn

D The type of activation function

The learning rate controls how big the model's updates are during training.

If the learning rate is too high, what is likely to happen?

A The model might never settle on a good solution

B The model learns very slowly

C The model uses less memory

D The model becomes simpler

A high learning rate can cause the model to jump around and miss the best solution.

What is the purpose of a learning rate schedule?

A To change the learning rate during training

B To increase the number of training samples

C To add more layers to the model

D To change the loss function

A learning rate schedule adjusts the learning rate over time to improve training.

Which is a sign that the learning rate might be too low?

A Model accuracy is very high immediately

B Training loss jumps up and down

C Model uses less data

D Training is very slow and stuck

A low learning rate can cause slow progress and the model might get stuck.

How can you find a good learning rate?

A Use the biggest rate possible

B Try different rates and watch training progress

C Always use a fixed rate of 0.1

D Ignore learning rate and focus on model size

Testing different learning rates helps find one that works well for the model.

Explain why choosing the right learning rate is important for training a model.

Think about how fast or stable the model learns.

Describe what a learning rate schedule does and why it might help.

Consider how changing speed helps when learning something new.