Aprendizaje profundo: Entrenar redes neuronales y desplegar con Docker

Resumen

This Deep Learning and Neural Networks in Production course equips you with the skills to design, train, and deploy neural networks using PyTorch, TensorFlow, FastAPI, and Docker. Whether you're building models from scratch or serving them in production, this course bridges the gap between deep learning theory and real-world deployment.

In Module 1, you'll explore the foundations of neural networks — building and training feed-forward networks, understanding activations, losses, and optimizers in PyTorch. Module 2 focuses on robust training and validation loops, experiment tracking with TensorBoard and Weights & Biases, and checkpoint analysis.

Module 3 covers packaging trained models for inference, serving them via FastAPI, and evaluating latency and reliability. Module 4 teaches containerization with Docker, production monitoring, logging, and scaling strategies.

By the end of this course, you will:

- Design and train neural networks using PyTorch and TensorFlow - Track and visualize model performance using TensorBoard and Weights & Biases - Serve trained deep learning models through FastAPI for real-time inference - Package, deploy, and scale deep learning applications with Docker in production Disclaimer:

This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated.

The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

Programa

• Fundamentos de Redes Neuronales
Cubre los conceptos fundamentales de las redes neuronales, incluida la arquitectura, activaciones, pérdidas, optimizadores e implementación en PyTorch.
• Entrenamiento, Validación y Seguimiento del Modelo
Se centra en implementar bucles de entrenamiento y validación robustos, realizar el seguimiento de experimentos utilizando TensorBoard o Weights & Biases, y analizar puntos de control para obtener perspectivas.
• Despliegue de Modelos de Aprendizaje Profundo
Cubre el empaquetado de modelos entrenados de aprendizaje profundo para inferencia de API, despliegue de modelos a través de FastAPI, y prueba y medición del rendimiento de inferencia. Duración: 4 horas.
• Contenerización e Integración en Producción
Cubre la contenerización de APIs de aprendizaje profundo con Docker, integración de registro, manejo de errores y configuración, y despliegue y escalado de servicios de aprendizaje profundo en producción. Duración: 4 horas.

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Impartido por

Board Infinity

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Materias

Computer Science