Python Tutorial

Understand and implement the Adam optimizer in Python. Learn the intuition, math, and practical applications in machine learning with PyTorch

Python's walrus operator := allows you to assign a value to a variable within an expression, combining assignment and use in a single step.

Discover the distinct advantages of REST and GraphQL for modern API design. Learn when to leverage REST for simplicity and caching. Master the flexibility of GraphQL for complex, real-time data needs.

Learn how to implement binary search in Python using iterative and recursive approaches, and explore the built-in bisect module for efficient, pre-implemented binary search functions.

Control Python's print output to avoid automatic new lines. Learn how to override the default newline behavior using the end parameter, sys module, and string concatenation.

This complete guide teaches you the fundamentals of Python backend development. Learn basic concepts, frameworks, and best practices to start building web applications.

Boosting improves machine learning performance by sequentially correcting errors and combining weak learners into strong predictors.

Discover how to use \n to insert line breaks in strings and print() statements. Use the end parameter to keep your output on a single line. Improve your file handling skills with os.linesep for managing text across different systems.

Understand how to copy lists in Python. Learn how to use the copy() and list() functions. Discover the difference between shallow and deep copies.

The if __name__ == "__main__" block in Python allows you to define code that will only run when the file is executed directly as a script, but not when it's imported as a module into another script.

Learn how to round a number to two decimal places in Python for improved precision using techniques like round(), format(), and string formatting techniques.

Explore how to master hyperparameter tuning with Optuna. Learn how to define hyperparameters, set up your objective function, and utilize sampling and pruning techniques in deep reinforcement learning.