In this notebook we will be learning how to use Transfer Learning to create the powerful convolutional neural network with a very little effort, with the help of MobileNetV2 developed by Google that has been trained on large dataset of images.
We will be using the pretrained model to train our dataset on the MobileNetV2 model.
- Transfer Learning : It is a technique that reuses a model that was created by the machine learning experts and that has already been trained on a large dataset.
Note : When performing transfer learning we must always change the last layer of the pre-trained model so that it has the same number of classes that we have in the dataset we are working with.
- Freezing Parameters : Setting the variables of a pre-trained model to non-trainable. By freezing the parameters, we will ensure that only the variables of the last classification layer get trained, while the variabels from the other layers of the pre-trained model are kept the same.
- MobileNet : A state-of-the-art convolutional neural network developed by Google that uses a very efficient neural network architecture that minimizes the amount of memory and computational resources needed, while maintaining a high level accuracy. MobileNet is ideal for mobile devices that have limited memory and computational resources.
Check my Kaggle Notebook Link where you will get the understanding of Tranfer learning with the help of MobileNetV2.
Kaggle Link :Transfer Learning With MobileNetV2
Thank You !