Computation graph

As explained in Machine Learning Algorithms in Depth

A graph representation in which nodes and edges describe the composition and flow of computations.

A computation graph represents deep learning as connected computational layers. Sequential computation determines its depth, while parallel computation determines its breadth. section 1.1

The graph provides the structure for training and model interpretation:

• Deep learning models refine parameters through backpropagation until they meet an objective function. section 1.1
• Individual blocks represent components of a larger architecture and can be composed into models for complex prediction tasks. section 1.1

Sentiment-analysis network

A deep neural network for sentiment analysis illustrates a computation graph whose layers process information toward a prediction. section 1.1

Figure 1.3 Deep neural network (DNN) architecture for sentiment analysis

Use the architecture figure to inspect how a sentiment-analysis network is organized as connected computational blocks. section 1.1

How Machine Learning Algorithms in Depth covers it

How deep it goes

Defines depth and breadth and links graphs to backpropagation, but defers detailed treatment of individual computational blocks.

Examples

Uses a deep neural network architecture for sentiment analysis as its concrete graph example.

What you can do after

Evaluate how deep-learning layers support sequential computation, parallel computation, and parameter refinement.

How it connects

Connects computation graphs to deep learning, backpropagation, objective functions, and neural-network architectures.

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  Deep Learning with Python, Third EditionA directed acyclic graph that represents a computation as operations and intermediate values, enabling automatic differentiation.
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  Deep Learning with PyTorch, Second EditionA graph representation captured from model execution that records operations and data flow.
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Related concepts

• backpropagationused by · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
• directed graphkind of · A directed graph is a graph whose edges have an orientation from a source vertex to a destination vertex.
• Daskused by · Dask is an open-source Python library for parallel and distributed computing using task graphs and scalable data structures for large or partitioned workloads.
• scheduleruses · A scheduler is a system component that selects, orders, dispatches, or assigns execution resources to tasks according to timing, dependency, placement, or other scheduling criteria.
• directed acyclic graphkind of · A directed acyclic graph is a graph with directed edges and no directed cycles.
• PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.

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