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Project: Classification with Transfer Learning in Keras

Project: Classification with Transfer Learning in Keras

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8.3/10 (Our Score)
Product is rated as #180 in category Machine Learning

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. Rhyme is Coursera’s hands–on project–based learning platform. On Rhyme, learners get instant access to pre–configured cloud desktops containing all the software and data they need. Rhyme helps learners apply the knowledge they learned in other Coursera courses into specific tools and use–cases. So they become …

Instructor Details

Amit is a Machine Learning Engineer with focus in creating deep learning based computer vision and signal processing products. He has led chat bot development at a large corporation in the past. Amit is one of the Machine Learning and Data Science instructors at Rhyme.

Specification: Project: Classification with Transfer Learning in Keras

Duration

4 hours

Year

2020

Level

Intermediate

Certificate

Yes

Quizzes

Yes

2 reviews for Project: Classification with Transfer Learning in Keras

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  1. Ali E

    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.

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  2. Utkarsh R

    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.

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    Project: Classification with Transfer Learning in Keras
    Project: Classification with Transfer Learning in Keras

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