Learn about machine learning and artificial intelligence in JavaScript

As artificial intelligence and machine learning technologies continue to develop, they are becoming important technical tools in more and more fields, including JavaScript. Although JavaScript has been used as a high-level scripting language for many years, its application in the fields of machine learning and artificial intelligence is still in its infancy. This article will introduce machine learning and artificial intelligence in JavaScript and provide specific code examples.

1. What are machine learning and artificial intelligence?

Before we dive into the latest JavaScript machine learning and artificial intelligence technologies, let’s understand the general definitions of these terms.

Machine Learning:

Machine learning is an application of artificial intelligence that uses statistical theory and pattern recognition algorithms to allow computer systems to learn and improve from data without explicit Programming guidance.

Artificial intelligence:

Artificial intelligence is a technology that enables computer systems to process and make decisions on different data. It is a technology that uses computer algorithms to simulate and implement human intelligent behaviors, such as perception and reasoning. Applications of artificial intelligence can include various techniques such as machine learning.

2. Machine Learning and Artificial Intelligence in JavaScript

With developers’ increasing demand for big data and data analysis, artificial intelligence and machine learning technologies have become increasingly important in the JavaScript ecosystem. more and more important.

In JavaScript, there are many open source machine learning libraries, such as TensorFlow.js, Brain.js, ConvNetJS, etc., which can provide powerful support for AI development. These libraries are designed to help developers create machine learning models that are trained and improved in accuracy to process large amounts of data.

1. TensorFlow.js

TensorFlow.js is a JavaScript library developed by Google to allow front-end developers to use the power of TensorFlow. TensorFlow.js can help developers build deep learning models, including neural networks and convolutional neural networks. TensorFlow.js is widely used in cross-platform applications and smartphone applications.

The following is a code example for linear regression using TensorFlow.js:

//定义模型
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));

// 编译模型
model.compile({ loss: "meanSquaredError", optimizer: "sgd" });

// 准备训练数据
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// 训练模型
model.fit(xs, ys, { epochs: 200 }).then(() => {
  // 在控制台中显示根据给定训练数据训练出的模型系数
  console.log("TensorFlow.js linear regression model trained");
  const result = model.predict(tf.tensor2d([5], [1, 1]));
  result.print();
});

2. Brain.js

Brain.js is a powerful JavaScript Library that helps developers create machine learning models for a variety of applications. It is developed by Andrei Kashcha and other contributors. Brain.js supports many different types of machine learning models, namely neural networks, recurrent neural networks, and convolutional neural networks.

The following is a code example for neural network classification using Brain.js:

const brain = require("brain.js");

//准备训练数据
const data = [
  { input: { r: 0.62, g: 0.72, b: 0.88 }, output: { light: 1 } },
  { input: { r: 0.1, g: 0.84, b: 0.72 }, output: { light: 1 } },
  { input: { r: 0.33, g: 0.24, b: 0.29 }, output: { dark: 1 } },
  { input: { r: 0.74, g: 0.78, b: 0.86 }, output: { light: 1 } },
  { input: { r: 0.31, g: 0.35, b: 0.41 }, output: { dark: 1 } },
  { input: { r: 1, g: 0.99, b: 0 }, output: { light: 1 } },
  { input: { r: 1, g: 0.42, b: 0.52 }, output: { dark: 1 } },
];

// 训练神经网络
const net = new brain.NeuralNetwork();
net.train(data);

// 确定特定的RGB颜色值是可以分类为浅色或深色
const output = net.run({ r: 0.1, g: 0.84, b: 0.72 });
console.log(output);  // { light: 0.991987407207489 }

The above are just two JavaScript machine learning libraries, we can also use many other JavaScript libraries for more Machine learning and artificial intelligence tasks.

3. Conclusion

Although JavaScript has become one of the most widely used programming languages ​​in the world, it is still an emerging field in the field of machine learning and artificial intelligence, because Its ecosystem of machine learning and artificial intelligence technologies is underdeveloped compared to other languages.

In our text, we have taken a look at two very powerful JavaScript machine learning libraries, TensorFlow.js and Brain.js. Using them, we can develop various machines in the JavaScript ecosystem. Learning and artificial intelligence applications.

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