Machine learning

As explained in Grokking AI Applications

An area of artificial intelligence in which machines learn patterns or behavior autonomously from data rather than following only fixed rules.

Machine learning enables AI systems to learn patterns from data and make predictions or decisions instead of following a separate fixed rule for every case. Its outputs remain probabilistic: a system might estimate a 72% chance that a customer will purchase a product. section 1.2

Machine learning commonly takes three forms:

• Unsupervised learning finds patterns and structures in unlabeled data. section 1.5
• Supervised learning learns from labeled examples to predict outcomes. section 1.5
• Reinforcement learning interacts with an environment, using trial and error to pursue goals. section 1.5

From language patterns to conversation

ChatGPT illustrates a staged machine-learning system. GPT first predicts likely next words through unsupervised learning; later supervised and reinforcement-learning steps refine its ability to sustain human conversations. section 1.5

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

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

How Grokking AI Applications covers it

How deep it goes

Defines three learning types and staged language-model training, but stops short of detailed algorithm internals.

Examples

Uses customer-purchase prediction, toy-shape grouping, child color learning, walking by trial and error, and ChatGPT training.

What you can do after

Evaluate/understand how data-driven prediction differs from fixed rules and how ChatGPT combines learning stages.

How it connects

Connects machine learning with artificial intelligence, generative AI, large language models, supervised learning, and reinforcement learning.

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Related concepts

• artificial intelligencekind of · Artificial intelligence is the field concerned with creating computational systems that perform tasks associated with human intelligence, such as learning, reasoning, perception, or decision-making.
• language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• supervised learningincludes · Supervised learning is a machine-learning approach that learns to map inputs to target outputs from examples paired with known labels or values.
• reinforcement learningincludes · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
• deep learningincludes · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• datauses · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.

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.