Machine learning

As explained in Introduction to Generative AI, Second Edition

Machine learning is the computational use of algorithms that learn patterns from data to improve performance on tasks.

How Introduction to Generative AI, Second Edition covers it

How deep it goes

Defines three learning paradigms and gives historical context, but stops short of implementation details or mathematical treatment.

Examples

Uses language translation, the Turing test, ELIZA, and the three-way learning comparison as concrete illustrations.

What it assumes

Introduces algorithms, data, task improvement, and learning signals without assuming prior machine learning knowledge.

What you can do after

Evaluate/understand how learning signals shape tasks and why conversational fluency does not prove reasoning.

How it connects

Connects machine learning with natural language processing, artificial intelligence, supervised learning, unsupervised 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 Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.