Machine Learning Tutorial

Understand how the F1 score evaluates model performance by combining precision and recall. Learn its use in binary and multiclass classification, with Python examples.

Learn how to convert models to ONNX format, optimize them with quantization, and deploy them across any platform - from edge devices to cloud servers - without vendor lock-in.

Learn how UMAP simplifies high-dimensional data visualization with detailed explanations, practical use cases, and comparisons to other dimensionality reduction methods, including t-SNE and PCA.

This guide explains the mathematical intuition behind the tanh function, how it compares to sigmoid and ReLU, its advantages and trade-offs, and how to implement it effectively in deep learning.

This guide explains the mathematical properties of Softplus, its advantages and trade-offs, implementation in PyTorch, and when to switch from ReLU.

Understand discrete probability distributions in data science. Explore PMF, CDF, and major types like Bernoulli, Binomial, and Poisson with Python examples.

Feed-Forward Neural Networks (FFNNs) are the foundation of deep learning, used in image recognition, Transformers, and recommender systems. This complete FFNN tutorial explains their architecture, differences from MLPs, activations, backpropagation, real-world examples, and PyTorch implementation.

Learn how blue-green deployment enables near-zero downtime, simple rollbacks, and safe production testing in modern DevOps and cloud-native workflows.

Learn what multi-head attention is, how self-attention works inside transformers, and why these mechanisms are essential for powering LLMs like GPT-5 and VLMs like CLIP, all with simple examples, diagrams, and code.

Learn how Vision Transformers (ViTs) leverage patch embeddings and self-attention to beat CNNs in modern image classification. This in-depth tutorial breaks down the ViT architecture, provides step-by-step Python code, and shows you when to choose ViTs for real-world computer-vision projects.

Learn what Maximum Likelihood Estimation (MLE) is, understand its mathematical foundations, see practical examples, and discover how to implement MLE in Python.

Explore KL-Divergence, one of the most common yet essential tools used in machine learning.