Frameworks like PyTorch and TensorFlow through TensorFlow Fold support Dynamic Computational Graphs and are receiving attention from data scientists.However, there seems to be a lack of resource to aid in understanding Dynamic Computational Graphs.The advantage of Dynamic Computational Graphs appears to include the ability to adapt to a varying quantities in input data.  It seems like there may be automatic selection of the number of layers, the number of neurons in each layer, the activation function, and other NN parameters, depending on each input set instance during the training.  Is this an accurate characterization?What are the advantages of dynamic models over static models?  Is that why DCGs are receiving much attention?  In summary, what are DCGs and what are the pros and cons their use?
What is a Dynamic Computational Graph?
neural networks