Transfer learning is commonly used in deep learning applications. You can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network from scratch with randomly initialized weights. You can quickly transfer learned features to a new task using a smaller number of training images.
-> AlexNet
-> Vgg16
-> Vgg19
-> ResNet-50
-> ResNet-101
-> GoogleNet
-> Inception-v3
-> Inception-ResNet-v2
Step 1: The last three layers : "Fully-Connected-Layer", "SoftMax" and "Classification Predictions" are removed.
Step 2: New three layers : "Fully-Connected-Layer", "SoftMax" and "Classification Predictions" are added but based on number of classes in our dataset.
Step 3: Connect Original Network's "Pooling-Layer" to newly created layers in Step 2
The difference in the using different neural network implementation (as given above) is defining the neural network model and the identification of these layers and replacing them.
https://www.mathworks.com/help/nnet/ug/pretrained-convolutional-neural-networks.html