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Even among binary image classification tasks, some tasks are easier for a neural network to learn than others. Easier tasks can be learned with fewer training examples.

Easier Component Tasks Need Fewer Training Examples

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Machine learning does not yet have a good formal definition of task difficulty. Informally, a task can be called easy if it can be carried out with fewer computation steps, corresponding to a shallow neural network, and hard if it needs more computation steps, corresponding to a deeper neural network.

Informal Neural Network Task Difficulty

Which binary classification task is considered easiest and therefore learnable with the fewest training examples?

According to ML Yearning, easier binary classification tasks require fewer training examples than harder binary classification tasks.

You will be able to learn _____ tasks with fewer training examples.

Match each binary classification task to its relative difficulty position in ML Yearning's ordered list (1 = easiest, 5 = hardest).

Order these five binary classification tasks from easiest (1) to hardest (5) as listed in ML Yearning.

Why does task difficulty matter when choosing which subtask to assign to a pipeline component, according to ML Yearning?

According to ML Yearning, classifying whether an image contains a Siamese cat is easier for a neural network than classifying whether an image was taken indoors or outdoors.

Each of the five example tasks in ML Yearning is a _____ image classification task: input an image and output either 0 or 1.

Match each task characteristic to the binary classification task it best describes from ML Yearning's five-task difficulty list.

Order the steps a practitioner should follow when applying ML Yearning's task-simplicity principle to select a pipeline component's task formulation.

Question:
In the context of binary image classification tasks, analyze how the inherent difficulty of a specific task impacts the amount of training data required. Support your analysis with at least two examples of varying difficulty.

Sample answer:
The inherent difficulty of a task directly dictates the volume of training data needed. Easier tasks require fewer training examples to reach a given level of performance, while complex tasks demand significantly more data. For instance, determining if an image is overexposed is a relatively simple task that a network can learn with a modest dataset. In contrast, identifying a specific breed of cat, like a Siamese cat, is much harder and requires vastly more examples for the model to learn the nuanced distinguishing features.

Key points:
- Easier tasks require fewer training examples.
- More complex tasks require significantly larger datasets.
- Specific examples must be provided to contrast difficulty levels.

Rubric:
The essay should clearly establish the relationship between task simplicity and training data requirements. It must provide at least two examples of binary image classification tasks of varying difficulty (e.g., overexposed image vs. Siamese cat) to illustrate the point.

Impact of Task Difficulty on Data Requirements

Case context:
You are building a computer vision pipeline with a very limited budget for manually labeling training data. You must decide whether to train a component to classify if an image simply 'contains a bird' or to classify if it 'contains a Northern Cardinal'.

Question:
Based on the principles of task simplicity, which classification subtask should you select for this component given your data constraints, and why?

Sample answer:
You should select the subtask to classify whether the image 'contains a bird.' Classifying the general presence of a bird is an easier binary classification task than identifying a specific species. Because easier tasks can be learned with fewer training examples, it is the only feasible option given the limited budget for labeling data.

Key points:
- Select the 'contains a bird' task.
- Identify it as an easier task than species-level identification.
- Explain that easier tasks require fewer training examples, satisfying the constraint.

Rubric:
The response must correctly choose the easier task ('contains a bird') and justify the choice by explaining that easier tasks require fewer training examples, which aligns with the limited data labeling budget.

Selecting a Task Under Data Constraints

Question:
Why would a neural network trained to classify if an image is taken indoors or outdoors likely require a different number of training examples than one trained to classify if an image contains a cat with black and white fur?

Sample answer:
Classifying whether an image was taken indoors or outdoors is inherently easier for a neural network than identifying a cat with specific fur colors. According to machine learning principles, easier tasks can be learned with fewer training examples, so the indoor/outdoor task would require less data.

Key points:
- The two tasks have different levels of difficulty.
- Easier tasks require fewer training examples to learn.

Rubric:
The answer must mention that the tasks have different inherent difficulty levels and state the rule that easier tasks can be learned with fewer training examples.

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