AI Sample Code Guide 🤖

Welcome to the AI Sample Code Guide! Here are some practical examples to help you get started with AI development:

Python Basics

# Simple AI model example
import tensorflow as tf
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(5,)),
    tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam', loss='mse')

🔍 View more Python ML tutorials

TensorFlow Demo

# TensorFlow MNIST example
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

📊 Explore TensorFlow datasets

PyTorch Example

# PyTorch linear regression
import torch
X = torch.tensor([[1.0], [2.0], [3.0]])
y = torch.tensor([2.0, 4.0, 6.0])

model = torch.nn.Linear(1, 1)
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

for epoch in range(100):
    predictions = model(X)
    loss = criterion(predictions, y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

🧠 Learn PyTorch fundamentals

Visual Aids

For advanced topics, check out our AI Framework Comparison guide!