Transfer Learning for Image Classification – Part1

One of the most useful and emerging applications in the ML domain nowadays is using the transfer learning technique; it provides high portability between different frameworks and platforms. Once you’ve trained a neural network, what you get is a set of trained hyperparameters’ values. For example, LeNet-5 has 60k parameter values, AlexNet has 60 million, and VGG- 16 has about 138 million […]

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Deep-belief network with DL4J

A deep-belief network can be defined as a stack of restricted Boltzmann machines in which each RBM layer communicates with both the previous and subsequent layers. The nodes of any single layer don’t communicate with each other laterally. This stack of RBMs might end with a Softmax layer to create a classifier, or it may […]

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