Classification with Transfer Learning in Keras

4.5
estrellas

153 calificaciones

ofrecido por

5479 ya inscrito

En este proyecto guiado, tú:
2 hours
Intermedio
No se necesita descarga
Video de pantalla dividida
Inglés (English)
Solo escritorio

In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training. In order to be successful in this project, you should be familiar with Python, Neural Networks, and CNNs. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Habilidades que desarrollarás

  • Deep Learning

  • Inductive Transfer

  • Convolutional Neural Network

  • Machine Learning

  • Tensorflow

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

Instructor

Reseñas

Principales reseñas sobre CLASSIFICATION WITH TRANSFER LEARNING IN KERAS

Ver todas las reseñas

Preguntas Frecuentes