Chevron Left
Zurück zu Classification with Transfer Learning in Keras

Kursteilnehmer-Bewertung und -Feedback für Classification with Transfer Learning in Keras von Coursera Project Network

42 Bewertungen
6 Bewertungen

Über den Kurs

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....


Filtern nach:

1 - 6 von 6 Bewertungen für Classification with Transfer Learning in Keras

von sara l k

May 29, 2020

Everything was as per description! Need more advanced tasks. Thanks, Amit Sir!

von M V

Jun 03, 2020

Great course, surely learnt a lot.


May 02, 2020


von Ali E

Mar 22, 2020

Good course, but still misses a key step: how to save and reuse the modified model without having to rebuild it from scratch? Literature about this topic is at best ambiguous if not flat out lacking. You should include the method for saving and reloading customized models with custom layers and/or standard layers that have been added to the pre-trained models.

von Utkarsh R

Mar 24, 2020

Learning a topic using Hands on project is way better than passive learning in my opinion. Explanation could've been much better. They can use slides and animation to explain the core functioning of objects.

von Manoj K S

May 08, 2020

Its first time I went to the Keras and TensorFlow they are super easy to implement.