Supervised learning

As explained in Grokking AI Applications

Supervised learning is a machine learning approach that learns to predict outcomes from labeled examples.

Supervised learning predicts outcomes by learning from labeled examples: given past inputs and desired answers, a model adjusts its behavior toward those answers. It contrasts with unsupervised learning, which discovers patterns without labels, and reinforcement learning, which improves through trial and error. section 1.5

A typical supervised-learning workflow for fine-tuning a pretrained model includes:

• Provide numerous prompts together with their desired completions. section 1.6
• Run learning passes that adjust the neural network’s parameters toward the desired completions. section 1.6
• Use the resulting model for more specialized tasks, knowledge, or human dialogue. section 1.6

Fine-tuning a conversational model

A pretrained GPT model can learn better conversational behavior from many prompt-and-completion examples. Supervised training refines its initial output so it follows instructions and sustains dialogue more effectively. section 1.6

Figure 1.5 The three types of machine learning: unsupervised, supervised, and reinforcement

Compare supervised learning with unsupervised and reinforcement learning in one visual overview. section 1.5

How Grokking AI Applications covers it

How deep it goes

Explains labeled examples and parameter adjustment through backpropagation-like passes, without detailing neural-network internals.

Examples

Uses child-guided color learning, prompt-and-completion datasets, and GPT conversational fine-tuning as examples.

What it assumes

Assumes a basic understanding of machine learning while defining labels, predictions, and learning types from intuitive examples.

What you can do after

Apply supervised learning to fine-tune a pretrained model with prompts and desired completions.

How it connects

Connects supervised learning with unsupervised learning, reinforcement learning, fine-tuning, neural networks, and backpropagation.

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Related concepts

• machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
• unsupervised learningcontrasts with · Unsupervised learning is a machine-learning approach that discovers patterns, structure, or representations in data without labeled target outputs.
• labeled datauses · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
• reinforcement learning with human feedbackused by · Reinforcement learning with human feedback is a model-training approach that uses human evaluations or preferences to guide optimization of model behavior.
• classifierincludes · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
• linear regressionincludes · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

This explainer was generated by Manning's knowledge graph from the text of Grokking AI Applications. The section links point to the parts of the book it draws on.