What are network topologies in deep learning
This recipe explains what are network topologies in deep learning
Recipe Objective - What are network topologies in deep learning?
Network topology defines a hypothesis space. Network topology constraint space of possibilities or hypothesis space to the specific series of tensor operations giving output data based on input data. Values of weight tensors involves in tensor operations requires a good set of values. Network topology consists of Two-branch networks, multihead networks, inception blocks etc.
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Explanation of network topologies.
Multi-head neural networks or multi-head deep learning model are also known as the multi-output deep learning models. It is used in problems where there are multiple labels for each feature row in the data.
Inception blocks is an image model block which aims to approximate the optimal local sparse structure in CNN. It allows use of multiple types of filter size, instead of restricted to the single filter size in the single image block which can be concatenated and pass onto a next layer.
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