Selenium vs TensorFlow Comparison Guide for Beginners

# Selenium (Python) — basic login test
from selenium import webdriver
from selenium.webdriver.common.by import By

driver = webdriver.Chrome()
driver.get("https://example.com/login")
driver.find_element(By.ID, "user").send_keys("test")
driver.find_element(By.ID, "pass").send_keys("secret")
driver.find_element(By.ID, "login").click()
assert "Dashboard" in driver.title
driver.quit()

// Selenium (Java) — basic login test
import org.openqa.selenium.*;
import org.openqa.selenium.chrome.ChromeDriver;

public class LoginTest {
    public static void main(String[] args) {
        WebDriver driver = new ChromeDriver();
        driver.get("https://example.com/login");
        driver.findElement(By.id("user")).sendKeys("test");
        driver.findElement(By.id("pass")).sendKeys("secret");
        driver.findElement(By.id("login")).click();
        driver.quit();
    }
}

import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Input(shape=(4,)),
    layers.Dense(16, activation="relu"),
    layers.Dense(3, activation="softmax"),
])

model.compile(
    optimizer="adam",
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)

# Training the model
history = model.fit(
    X_train, y_train,
    epochs=20,
    batch_size=32,
    validation_split=0.2,
)

loss, acc = model.evaluate(X_test, y_test)
print(f"Accuracy: {acc:.3f}")

Quick comparison:
Selenium   → browser testing
TensorFlow → ML models
Both       → rarely used together directly
Not rivals → different stages of work

Beginner mistakes:
- Comparing them as alternatives
- Skipping Python before TensorFlow
- Skipping Java/Python before Selenium
- Trying to test UI with TensorFlow
- Trying to train models with Selenium

Testing workflow:
1. Write web code
2. Add Selenium tests
3. Run locally
4. Run in CI (headless)
5. Review reports
6. Ship with confidence

ML workflow:
1. Collect + clean data
2. Build a TensorFlow model
3. Train on GPU/TPU
4. Evaluate on test data
5. Deploy the model
6. Monitor + retrain