Documentation
Auth Process with bcrypt
Bcrypt is a widely used library for hashing passwords. It uses a cryptographic hashing function that makes it difficult to reverse-engineer the original password from the hash. The hash is stored in the database instead of the plain text password.
Example:
const bcrypt = require('bcrypt');
const saltRounds = 10;
const myPlaintextPassword = 's0me$tr0ngP@ssw0rd';
// Hashing a password
bcrypt.hash(myPlaintextPassword, saltRounds, function(err, hash) {
// Store hash in your password DB.
});
// Comparing passwords
bcrypt.compare(myPlaintextPassword, hash, function(err, result) {
// result is true if the passwords match
});
Creating New API Endpoints
In Next.js, API routes are created by adding files to the `pages/api` directory. Each file corresponds to an endpoint and exports a function to handle requests.
Example:
// pages/api/hello.js
export default function handler(req, res) {
res.status(200).json({ message: 'Hello World' });
}
Understanding Cookies and JWT
Cookies: Cookies are small pieces of data sent from a server and stored on the client's browser. They are often used for session management and tracking user preferences.
JWT (JSON Web Tokens): JWTs are a compact, URL-safe means of representing claims between two parties. They consist of a header, payload, and signature, and are commonly used for authentication and authorization.
Example:
const jwt = require('jsonwebtoken');
const token = jwt.sign({ username: 'user' }, 'secretKey', { expiresIn: '1h' });
const decoded = jwt.verify(token, 'secretKey');
In-Depth on Machine Learning
Machine Learning (ML) involves using data and algorithms to mimic human learning processes. ML models are trained on data to make predictions or decisions without being explicitly programmed for specific tasks.
TFJS (TensorFlow.js): TensorFlow.js is a JavaScript library for training and deploying machine learning models in the browser and on Node.js. It allows for the creation and use of complex ML models directly in web applications.
TFX (TensorFlow Extended): TensorFlow Extended is a comprehensive machine learning platform designed for production environments. It includes tools and libraries for managing ML pipelines, including data validation, preprocessing, model training, and deployment.
TFX Addons: TFX Addons provide additional components and integrations that extend the functionality of TFX, offering more flexibility and capabilities for managing ML workflows.
Example:
// Basic TensorFlow.js example
import * as tf from '@tensorflow/tfjs';
const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, inputShape: [1] }));
model.compile({ optimizer: 'sgd', loss: 'meanSquaredError' });
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);
model.fit(xs, ys, { epochs: 10 }).then(() => {
model.predict(tf.tensor2d([5], [1, 1])).print();
});