 -> Building systems that have dynamic structure in the graph is a strategy for accelerating data-processing systems. These systems dynamically determine neural network should be run on an input. Conditional Computation refers to the form of dynamic structure inside neural networks. 

-> A strategy for speeding up an inference in a classifier is to employ a cascade of classifiers. This strategy may be used to detect the presence of a rare object.

-> Since decision tree determines which of its subtrees should be evaluated for each input, its an example of dynamic structure. Switch, where a hidden unit can receive input from diﬀerent units depending on the contex, is also a dynamic structure. 

-> One disadvantage of using dynamically structured systems is the reduced degree of parallelism that results from the system following diﬀerent code branches for diﬀerent inputs. 

University of California, Davis

In this node, we describe how to use deep learning to solve applications in computer vision, speech recognition, natural language processing, and other areas of commercial interest. 

Applications of Deep Learning

Its crucial that the time and memory cost of running inference in a ML model is lower than the time and memory cost of training. Model compression is a strategy for reducing the cost of inference. Its main idea is to replace the original, expensive model with a smaller model that requires less memory and runtime to store and evaluate. This strategy can be used when size of the original model needs to prevent overfitting. 

Model Compression

Dynamic Structure

On the frontier of deep learning applications, research is being done on the representation of knowledge in relational manners to learn reasoning.

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