Python Basics for Deep Learning

# CHAPTER 3

Python Basics for Deep Learning #

1. Introduction #

2. Learning Objectives #

• Define variables and identify core data types.

• Control program flow using if/else conditions.

• Iterate through data using for and while loops.

• Store collections of data in Lists and Dictionaries.

• Write reusable functions.

3. Variables and Data Types #

# Integer (Epochs usually must be whole numbers)
epochs = 100

# Float (Learning rates are almost always decimals)
learning_rate = 0.001

# String (Text data)
activation_function = "relu"

# Boolean (True/False flags)
use_gpu = True

# Printing variables
print(f"Training for {epochs} epochs using {activation_function}.")

4. Data Structures: Lists #

# A list of loss values after each training step
loss_history = [0.95, 0.82, 0.71, 0.55, 0.42]

# Accessing the first item (0-indexed)
print(loss_history[0])  # Output: 0.95

# Accessing the last item
print(loss_history[-1]) # Output: 0.42

# Slicing (getting the first 3 items)
print(loss_history[0:3]) # Output: [0.95, 0.82, 0.71]

# Appending a new value
loss_history.append(0.35)

5. Data Structures: Dictionaries #

# Storing model configuration
model_config = {
    "layers": 3,
    "neurons_per_layer": 64,
    "optimizer": "adam"
}

# Accessing a value by its key
print(model_config["optimizer"]) # Output: adam

# Updating a value
model_config["layers"] = 5

# Adding a new key
model_config["dropout_rate"] = 0.2

6. Conditions (If / Else) #

current_accuracy = 0.92
target_accuracy = 0.95

if current_accuracy >= target_accuracy:
    print("Target reached! Stopping training.")
elif current_accuracy > 0.85:
    print("Getting close. Continue training.")
else:
    print("Accuracy is too low. Check the data.")

7. Loops (For and While) #

# A 'for' loop iterating over a list
image_classes = ["Cat", "Dog", "Bird"]
for item in image_classes:
    print(f"Processing image of a {item}...")

# A 'for' loop using range (commonly used for epochs)
# This loops 3 times: 0, 1, 2
for epoch in range(3):
    print(f"Running Epoch {epoch + 1}")
    
# Output:
# Running Epoch 1
# Running Epoch 2
# Running Epoch 3

8. List Comprehensions #

# Raw pixel values (0-255)
pixels = [0, 128, 255]

# Normalize pixels to be between 0 and 1
normalized = [p / 255.0 for p in pixels]

print(normalized) 
# Output: [0.0, 0.50196, 1.0]

9. Functions #

def normalize_image(pixel_array):
    """
    Takes an array of pixels and scales them to 0-1.
    """
    clean_array = [p / 255.0 for p in pixel_array]
    return clean_array

# Calling the function
raw_image = [50, 100, 200]
ready_image = normalize_image(raw_image)
print(ready_image)

10. Common Mistakes #

• Indentation Errors: Python does not use {} brackets to define code blocks like C++ or Java. It uses whitespace (indentation). If you forget to indent the code inside a for loop or if statement, Python will throw an IndentationError.

• Modifying a list while looping over it: Removing items from a list while currently iterating through it will cause the loop to skip items or crash.

11. Best Practices #

• Type Hinting: While Python doesn't require you to declare types, adding "Type Hints" makes deep learning code much easier to read and debug. (e.g., def train(epochs: int) -> float:)

• Use standard naming conventions: Variables should be snakecase. Classes should be PascalCase.

1. 1. Create a dictionary that holds the hyperparameter settings for a mock model: learningrate of 0.01, batch_size of 32, and optimizer as "sgd".

1. 2. Write a for loop that iterates from 1 to 5, printing "Training Epoch [number]" for each iteration.

13. MCQ Quiz with Answers #

14. Interview Questions #

• Q: Explain the difference between a List and a Dictionary in Python.

• Q: What is a List Comprehension, and why is it preferred over a standard for loop for simple transformations?

15. FAQs #

16. Summary #

17. Next Chapter Recommendation #