Network topology
Network topology is the structural arrangement and connectivity relationships among the components of a network or system.
Deep Learning with R, Third Edition
Network topology is the structural arrangement and connectivity pattern of a neural network's layers and operations.
- How it is used
- Defines how layers connect and constrains the functions a model can learn for a task.
- How deep it goes
- Explains topology through stacks, branches, feature hierarchies, and architecture assumptions; defers detailed construction of nonsequential models.
- Examples
- Uses an R/Keras Dense classifier, a Transformer layer graph, and repeated Xception convnet blocks.
- What you can do after
- Evaluate/understand whether an architecture’s connections and assumptions suit the data, then tune layers using validation results.
Read how Deep Learning with R, Third Edition explains network topology →
Secure APIs
Network topology is the arrangement and connectivity relationships among networks, hosts, and communication components.
- How it is used
- Shapes API security threat models by controlling exposure, permitted traffic, and movement between infrastructure components.
- How deep it goes
- Defines segmentation, public and private subnets, bridges, and DMZs, but stops short of comprehensive network-security coverage.
- Examples
- Uses segmented public and private networks, DMZ gateways, bastion servers, and separated public, internal, and development endpoints.
- What you can do after
- Evaluate API infrastructure, restrict sensitive resources, isolate services, and channel external traffic through controlled front-facing components.
Kafka for Architects
A graph topology is a directed processing structure that defines how streaming records traverse processing steps.
- How it is used
- Defines how streaming records flow through processing steps, from input Kafka topics to output topics.
- How deep it goes
- Explains graph nodes, edges, state stores, task assignments, and event traversal through the processing graph.
- Examples
- Illustrates source-to-sink event flow and custom processing for anomaly detection or fraudulent-transaction detection.
- What you can do after
- Build it from scratch by defining processors and their connections, or test a topology with TopologyTestDriver.
Related concepts
- directed acyclic graphkind of · A directed acyclic graph is a graph with directed edges and no directed cycles.
- full mesh topologyincludes · A full mesh topology is a network arrangement in which every node has a direct connection to every other node.
- TensorBoardused by · TensorBoard is a web-based tool for visualizing and monitoring machine-learning experiments and their logged data, including metrics, model graphs, and other run information.
- hypothesis spaceconfigures · A hypothesis space is a set of candidate functions, models, parameter configurations, or rules that a learning or inference procedure can represent or consider.
- star topologyincludes · A star topology is a network arrangement in which multiple peripheral nodes connect and communicate through a central hub or node.
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