01 - CNN Fundamentals: Architecture, Training, Deployment

Complete guide to CNN Fundamentals: Architecture, Training, Deployment: architecture, practical implementation and best practices for developers and technical teams.

What you'll learn

• CNNs exploit locality, weight sharing, and spatial invariance to process images efficiently
• The standard architecture follows the pattern: Conv + BatchNorm + ReLU + Pooling, repeated with increasing depth
• Skip connections (ResNet) are essential for training deep networks without vanishing gradients
• Transfer learning is almost always preferable to training from scratch, especially with limited data
• Data augmentation is critical for generalization and costs nothing in terms of data collection

This article is part of the Computer Vision series on federicocalo.dev.

Read the full article

The complete article (17 min read) with code examples, diagrams, and practical exercises is available here:

➡️ 01 - CNN Fundamentals: Architecture, Training, Deployment

https://federicocalo.dev/en/blog/cnn-fundamentals-architecture-training-deployment

By Federico Calò — Software Developer & Technical Writer