Machine Learning Tutorial

Master the inner workings of LSTM networks, the foundation for modern LLMs. Explore gating mechanisms, gradients, and build a sentiment classifier with PyTorch.

Stop vanishing gradients and biased models. Learn how to normalize data using min-max and z-score in Scikit-learn to improve machine learning models.

Accuracy isn't enough. Learn the difference between precision and recall, understand the trade-off, and choose the right metric for your model.

Learn what cost functions are, and how and when to use them. Includes practical examples.

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.