How deeply does Deep Learning with Python, Third Edition cover machine learning problem?

Defines framing questions and task categories, including data availability, but does not detail model implementation.

Machine learning problem

As explained in Deep Learning with Python, Third Edition

A task framed so that a model can be trained on data to make useful predictions or decisions.

A machine learning problem starts with a real-world need, not necessarily a ready-made dataset. Frame the inputs, the desired prediction or decision, the available training data, and the measure of success before choosing a model. section 6.0 section 6.1.1

From request to task type

A spam detector becomes a binary classification problem; photo search becomes multiclass, multilabel classification; click-through prediction becomes scalar regression; archaeological-site discovery becomes image-similarity ranking. section 6.1.1

Use these checks when framing a machine learning problem:

Identify the input data and the exact quantity or decision to predict. section 6.1.1
Confirm that training examples and the necessary labels or annotations exist; otherwise collect and annotate data. section 6.1.1
Classify the task as binary, multiclass, multilabel, regression, segmentation, ranking, clustering, generation, or reinforcement learning. section 6.1.1
Check whether another approach, such as statistical analysis or collaborative filtering, fits better than deep learning. section 6.1.1

How Deep Learning with Python, Third Edition covers it

Cover of Deep Learning with Python, Third Edition

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Related concepts

business problemaddresses · A business problem is an organizational need, objective, constraint, or challenge that a proposed solution is intended to address.
solvable machine learning problemincludes · A solvable machine learning problem is a prediction task for which humans can answer the questions, most cases have a correct answer, and performance should not substantially exceed that of a trained human expert.
linear regressionaddressed by · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
training datasetaddresses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
digital imageuses · A digital image is visual content encoded as data, typically as a two-dimensional arrangement of pixels or equivalent visual elements.
labeluses · A label is an assigned target value, category, or reference annotation associated with an item, used to identify its expected outcome or support learning and evaluation.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with Python, Third Edition. The section links point to the parts of the book it draws on.