機械学習チュートリアル

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.

Learn to distinguish sensitivity and specificity, and appropriate use cases for each. Includes practical examples.

Explore what underfitting is, how to diagnose an underfitting model, and discover actionable strategies on how to fix underfitting, ensuring your models accurately capture data patterns and deliver reliable predictions.

Discover how the Apriori algorithm works, its key concepts, and how to effectively use it for data analysis and decision-making.

Learn feature engineering with this hands-on guide. Explore techniques like encoding, scaling, and handling missing values in Python.

Learn how forward propagation works in neural networks, from mathematical foundations to practical implementation in Python. Master this essential deep learning concept with code examples and visualizations.