Deep learning

As explained in Effective Data Analysis

Deep learning is a machine-learning approach that uses multilayered neural networks to learn representations and patterns from data.

Deep learning uses neural networks within AI systems to learn patterns from large datasets and support automated decisions or tasks. section 8.2.2

Deep learning can support:

• Automating repetitive work that would otherwise require human judgment. section 8.2.2
• Categorizing customer support tickets by priority and type. section 8.2.2
• Identifying bias risks when training data contains human biases. section 8.2.2

Figure 8.4 An automated system for categorizing data can employ anything from simple rules to sophisticated machine learning algorithms.

Compare simple rules with sophisticated machine-learning algorithms used for automated categorization. section 8.2.2

How Effective Data Analysis covers it

How deep it goes

Defines pattern-based automation and illustrates it with ticket categorization, but stops short of training or implementation details.

Examples

Uses automated support-ticket categorization by priority and type as its concrete example.

What you can do after

Evaluate automated categorization systems for efficiency gains and potential bias in their data-driven decisions.

How it connects

Connects neural networks with artificial intelligence, machine learning, algorithms, automated systems, and bias.

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Compare how 11 books explain deep learning →

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.
• 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.
• autoencoderincludes · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
• neural networkuses · A neural network is a computational model of interconnected, parameterized units that learns transformations from inputs to outputs.
• GPT modelincludes · A GPT model is a generative pretrained Transformer language model that produces text autoregressively by predicting successive tokens from preceding context.
• recurrent neural networkincludes · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.

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