Python Syntax and Variables
Learn Python's clean syntax and variable assignment with AI-focused examples.
Variables
Data Types
Operations
Python's syntax is designed to be readable and concise, making it ideal for AI development. Understanding variables, data types, and basic operations forms the foundation for more complex AI programming tasks.
# Python Basics for AI
# Variables and data types commonly used in AI
learning_rate = 0.001 # Float for model parameters
num_epochs = 100 # Integer for training iterations
model_name = "neural_net" # String for identification
is_training = True # Boolean for control flow
# Lists for storing data (common in AI)
features = [1.2, 3.4, 2.1, 4.5] # Input features
labels = [0, 1, 1, 0] # Target labels
layer_sizes = [784, 128, 64, 10] # Neural network architecture
# Dictionaries for configuration (very common in AI)
model_config = {
"architecture": "CNN",
"layers": 5,
"activation": "relu",
"optimizer": "adam"
}
# String operations for data processing
dataset_path = "/data/images/"
file_extension = ".jpg"
full_path = dataset_path + "image001" + file_extension
print(f"Loading model: {model_name} with {num_epochs} epochs")
Control Flow and Loops
Master conditional statements and loops for AI algorithm implementation.
Essential Control Structures for AI:
• if/elif/else: Decision making in algorithms
• for loops: Iterating through datasets and epochs
• while loops: Training until convergence
• List comprehensions: Efficient data processing
AI-Specific Use Cases:
• Training loops for machine learning models
• Data preprocessing and validation
• Hyperparameter tuning iterations
• Model evaluation and testing
# Control flow examples for AI applications
# Training loop example
for epoch in range(num_epochs):
total_loss = 0
for batch_idx, (data, target) in enumerate(train_loader):
# Forward pass
output = model(data)
loss = criterion(output, target)
total_loss += loss.item()
# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Early stopping condition
avg_loss = total_loss / len(train_loader)
if avg_loss < 0.01:
print(f"Converged at epoch {epoch}")
break
# Data preprocessing with conditionals
processed_data = []
for sample in raw_data:
if sample['quality'] > 0.8: # Quality threshold
# Normalize the data
normalized = (sample['value'] - mean) / std
processed_data.append(normalized)
elif sample['quality'] > 0.5:
# Apply noise reduction
denoised = apply_filter(sample['value'])
processed_data.append(denoised)
# Skip low quality samples
# List comprehension for feature extraction
features = [extract_features(img) for img in image_dataset
if img.size > (224, 224)]
Functions and Modules
Create reusable code with functions and organize AI projects with modules.
Function Design for AI:
• Pure functions for data transformations
• Higher-order functions for model composition
• Generator functions for memory-efficient data loading
• Decorator functions for monitoring and logging
Module Organization:
• Separate data preprocessing modules
• Model architecture definitions
• Training and evaluation utilities
• Visualization and plotting functions
# AI-focused function examples
# Data preprocessing function
def preprocess_image(image_path, target_size=(224, 224)):
"""
Load and preprocess an image for model input
"""
import cv2
import numpy as np
# Load image
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)