Classification with Transfer Learning in Keras

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En este proyecto guiado, tú:

How to implement transfer learning with Keras and TensorFlow

How to use transfer learning to solve image classification

Clock2 hours
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo 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 LearningInductive TransferConvolutional Neural NetworkMachine LearningTensorflow

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:

  1. Import Libraries and Helper functions

  2. Download the Pet dataset and extract relevant annotations

  3. Add functionality to create a random batch of examples and labels

  4. Create a new model with MobileNet v2 and a new fully connected top layer

  5. Create a data generator function and calculate training and validation steps

  6. Get predictions on a test batch and display the test batch along with prediction

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




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