Course · 7 chapters
Deep Learning
A decision-framework deep learning course for engineers. Choose PyTorch vs TensorFlow, judge depth vs classical ML, weigh transfer learning, and reason about CNNs. 7 chapters.
What you'll be able to do
- Choose between PyTorch and TensorFlow
- Know when deep learning beats classic ML
- Decide scratch training vs transfer learning
- Pick the right computer vision task
- Tune training hyperparameters
- Understand how CNNs see images
What's inside
- 1Frameworks de DL: PyTorch y TensorFlow
Una guía de decisión sobre los dos frameworks dominantes de deep learning — sus filosofías, ecosistemas y los factores prácticos que deberían orientar tu elección.
- 2Entrenar desde cero vs Transfer Learning
La primera decisión estratégica después de elegir deep learning: entrenar una red nueva desde cero o apoyarte en los hombros de gigantes preentrenados.
- 3DL vs ML: cuándo gana la profundidad
Un marco de decisión para elegir entre deep learning y machine learning clásico — basado en tus datos, capacidad de cómputo, plazos y necesidades de interpretabilidad.
- 4Deep Learning: empieza aquí
Una orientación de 12 minutos sobre el skill path de Deep Learning — por qué existe, qué construirás, cómo se conectan los seis capítulos y por dónde empezar.
- 5Tipos de tareas de visión
Clasificación de imágenes, detección de objetos y segmentación: elige la tarea de visión por computadora adecuada antes de elegir una arquitectura.
- 6Técnicas de entrenamiento para deep learning
El kit completo para entrenar redes neuronales: tamaño de lote, tasas de aprendizaje, funciones de pérdida, activaciones, optimizadores, regularización y parada temprana.
- 7CNNs (Redes Neuronales Convolucionales)
Diseña, entrena e interpreta la arquitectura que impulsa la visión por computadora moderna — desde la primera convolución hasta el despliegue en producción.
Frequently asked questions
- What will I learn in this deep learning course?
- You learn how to make the key decisions behind deep learning projects: choosing between PyTorch and TensorFlow, judging when depth beats classical ML, weighing transfer learning against training from scratch, selecting the right computer vision task, reasoning about training choices like learning rates and regularization, and understanding how CNNs are structured for vision.
- Is this a hands-on coding course or a conceptual one?
- It is a decision-framework course. The chapters focus on the trade-offs and reasoning behind framework choice, model strategy, vision tasks, and training, rather than walking through step-by-step coding labs. It assumes you can already write code on your own.
- Who is this deep learning path for?
- It is built for engineers and ML practitioners who want a clear mental model for deep learning decisions. It is part of the AI for Engineers track and assumes you are comfortable writing code and familiar with machine learning basics.
- How long is the course and is there a certificate?
- The path runs about 2.3 hours across 7 chapters, beginning with a short Start Here orientation chapter. When you finish the path you earn a certificate of completion you can share.
- Is this course free?
- No, this is a paid path available with an AI Academy by Anthropos subscription.
Earn a certificate
Complete all chapters to receive your certificate of completion.