Model architecture

As explained in Grokking Machine Learning

The structural specification of a neural network, including its layers, nodes, and activation functions.

A neural network’s architecture describes how its nodes are arranged and connected. Nodes are grouped into an input layer, one or more hidden layers, and an output layer; each layer connects to the non-bias nodes in the next. section 10.1

Figure 10.14 The general architecture of a neural network. The nodes are divided into layers, where the leftmost layer is the input layer, the rightmost layer is the output layer, and all the layers in between are hidden layers. All the nodes in a layer are connected to all the (non-bias) nodes in the next layer.

See the layer layout and the connections between successive layers. section 10.1

Each node acts as a classifier: it combines its inputs and bias, then applies an activation function. Later layers combine earlier classifiers’ outputs, allowing the network to form more complex decision boundaries than a single linear classifier. section 10.1 section 10.1

Classifying alien moods

A single line cannot separate happy and sad aliens based on counts of “aack” and “beep.” Combining two line-based classifiers with an output classifier separates the examples, illustrating how connected perceptrons form a network. section 10.1 section 10.1

How Grokking Machine Learning covers it

How deep it goes

Defines layer roles and connectivity, then explains how activation functions and successive classifiers shape decision boundaries.

Examples

Uses alien word counts and connected career, family, and happiness classifiers to illustrate network structure.

What it assumes

Builds on numerical features and perceptron classifiers while introducing neural networks through a simple classification task.

What you can do after

Understand how connected perceptrons and layers combine to classify data with more complex boundaries.

How it connects

Connects neural network architecture to perceptrons, activation functions, and linear classification.

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Related concepts

• transformer architectureincludes · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• Apache Kafkacontrasts with · Apache Kafka is a distributed event-streaming platform for publishing, storing, processing, and consuming streams of records.
• hexagonal architectureincludes · Hexagonal architecture is a software architecture pattern that isolates an application’s core logic from external systems through ports and adapters.
• 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.
• linear layerincludes · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
• architectural subsystemhas part · An architectural subsystem is a distinct, cohesive part of a larger system that provides related functionality through interacting components and defined boundaries.

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