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Classification with Transfer Learning in Keras
  • Level Intermediate
  • Duration 3 hours
  • Offered by

About

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.

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"Classification with Transfer Learning in Keras" is an engaging intermediate course in IT & Computer Science. Led by Coursera, this 1.5-hour project-based course teaches you to create and train a Convolutional Neural Network (CNN) using a pre-existing model architecture and pre-trained weights. Ideal for those looking to deepen their knowledge in neural networks, it is available for free.