Python Tutorial

Learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with PyTorch.

Discover the intricacies of the negative binomial distribution and its applications. Learn how to model count data effectively. Explore practical examples and visual aids to enhance your understanding.

Learn about Python generators to optimize memory usage and simplify workflows. Build efficient, real-world data pipelines and enhance your code’s performance.

Learn about the policy gradient theorem in RL and how to derive it mathematically. Implement an algorithm based on policy gradients to solve a simple RL environment in Gymnasium.

Learn about Python string interpolation, its purpose, and when to use it. Includes practical examples and best practices.

Python inheritance allows you to build new classes by reusing and extending the functionality of existing ones. Learn how to design parent-child class relationships, implement inheritance patterns, and apply techniques such as method overriding.

Master feature extraction techniques with hands-on Python examples for image, audio, and time series data. Learn how to transform raw data into meaningful features and overcome common challenges in machine learning applications.

Learn to create and customize Seaborn barplots in Python. Master essential techniques for visualizing categorical data relationships, from basic plots to advanced features.

Examine the mathematical principles behind random walks and explore their forms, from one-dimensional paths to biased and Gaussian models. Use Python to discover how these stochastic processes inform real-world phenomena in biology, physics, and finance.

Learn the hill climbing algorithm in Python. This guide covers types, limitations, and real-world AI applications with code examples.

Learn to code an unbeatable Tic-tac-toe AI using the Minimax algorithm in Python. This tutorial covers theory, implementation, and optimization, ideal for game AI enthusiasts.

Learn about Python lambda functions, their purpose, and when to use them. Includes practical examples and best practices for effective implementation.