mlgo - A Golang Machine Learning Library

Welcome to mlgo, a Golang machine learning library crafted from derived mathematical proofs obtained through DeepLearning.AI courses. This library serves as a proof-of-concept as a learning project for deeper understanding of the underlying concepts. Was greatly inspired by Keras, machine learning library for Python, while adapting to the idioms and conventions of Golang.

Key Features

1. Mathematical Foundation: Handwritten from derived mathematical proofs obtained from DeepLearning.AI courses, ensuring a solid theoretical basis.

2. Open-Source Origins: Information was gathered from free resources on the internet, articles and posts.

3. Proof-of-Concept: Not designed for extensive optimization, the library serves as a proof-of-concept to demonstrate the feasibility of implementing machine learning concepts in Golang.

Example Code

package main

import (
    "fmt"
    "log"

    "github.com/Hukyl/mlgo/activation"
    "github.com/Hukyl/mlgo/datasets"
    "github.com/Hukyl/mlgo/loss"
    "github.com/Hukyl/mlgo/metric"
    "github.com/Hukyl/mlgo/nn"
    "github.com/Hukyl/mlgo/nn/layers"
    "github.com/Hukyl/mlgo/utils"
)


func main() {
    parameters := utils.NeuralNetworkParameters{
        EpochCount:          30,
        InitialLearningRate: 0.01,
        AccuracyMetric:      metric.CategoricalAccuracy{},
    }

    // Preprocess data
    X_train, Y_train := prepareData(datasets.MnistDataset("your/path/file.csv"))

    // Define the structure of the layers
    l := []layers.Layer{
        layers.NewRandomDense(
            [2]int{784, 20},
            activation.Sigmoid{},
            layers.XavierUniformInitialization{},
        ),
        layers.NewRandomDense(
            [2]int{20, 10},
            activation.Sigmoid{},
            layers.XavierUniformInitialization{},
        ),
    }
    model := nn.NewNeuralNetwork(
        l,
        loss.LogLoss[float64]{},
    )

    // Train your network
    err := model.Train(X_train, Y_train, parameters)
    if err != nil {
        log.Fatal(err)
    }

    Y_pred := model.Predict(X_train[0])
    fmt.Println(parameters.AccuracyMetric.Calculate(Y_train[0], Y_pred))
    nn.DumpNeuralNetwork(model, "path/to/dump.json")
}

Author and Copyright

Andrii Shalaiev
Copyright © 2024 Andrii Shalaiev. All rights reserved.

References

• DeepLearning.AI course

• Debugging a Neural Network

Disclaimer

This library is a proof-of-concept and may not be suitable for production use. Use at your own risk.