Go-Deep

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go-deep is an artificial neural network library implemented in Go. It provides support for feedforward/backpropagation and various activation functions and solvers.

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At a glance

Pricing

—

Free tier

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API

Yes

Skill level

Technical

About

What is go-deep?

go-deep is an artificial neural network library built in the Go programming language. It offers core functionalities for creating and training neural networks, including support for both feedforward and backpropagation architectures. The library incorporates a range of activation functions such as sigmoid, hyperbolic tangent, and ReLU, allowing for flexibility in model design. Additionally, go-deep provides several optimization solvers, including Stochastic Gradient Descent (SGD), SGD with momentum and Nesterov acceleration, and Adam, to facilitate efficient model training.

Best used for

Building and training artificial neural networks directly within Go applications.

Common actions

Build neural networks

Implement machine learning

Train AI models

Develop with Go

automated workflowopen-sourcedeepfakelow-code/no-codeworkflowscollaborationgithub copilot"AI Agents"face swapping

Capabilities

Key features

Go neural network library
Feedforward/backpropagation
Multiple activation functions
Various optimization solvers

Target Audience

Go developersMachine learning engineersData scientists

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Does go-deep support GPU acceleration for training neural networks?

go-deep is primarily a CPU-based library for Go. While Go can interface with C/CUDA, direct GPU acceleration is not a built-in feature of go-deep itself. Performance will depend on CPU core count and optimization.

Can go-deep be used for deep learning models like CNNs or RNNs?

go-deep focuses on foundational feedforward and backpropagation networks. It provides the building blocks, but does not natively offer higher-level abstractions for complex architectures like Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs).

What are the typical use cases where go-deep would be preferred over Python-based deep learning frameworks?

go-deep is ideal for Go developers who need to integrate neural networks directly into Go applications, especially for scenarios requiring high concurrency, low latency, or minimal dependencies outside the Go ecosystem, where Python might introduce overhead.

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