Deep neural network training for application program generation

A method of deep neural network training for application program generation, the method comprising:

• presenting, by a trainer server, data about a particular subject to a user having knowledge about the particular subject;
• receiving, by the trainer server, user sensory input via one or more sensors measuring activities and reactions of the user related to the presented data as the data is presented to the user;
• creating, by the trainer server, an attention map based on the user sensory input, the attention map being indicative of portions of the presented data on which the user focused and including one or more data classification regions;
• annotating, by the trainer server, the attention map with natural language input labels based on a natural language input of the user;
• training, by the trainer server, a neural network based on the annotated attention map, the training including communicating to a differential memory subsystem neural network information and processed user sensory input for experimental data processing functions;
• creating, by the trainer server, a model based on the trained neural network that is optimized for an anomalous activity of an end use application;
• providing, by the trainer server, an application program for an output target of the end use application;
• and
• instructing, by the trainer server, the output target via the application program to detect and remedy an anomalous activity.

A non-transitory computer readable medium having stored therein executable code that, in response to being executed by a processor, causes a system to perform operations, the operations comprising:

• presenting, by a trainer server, data about a particular subject to a user having knowledge about the particular subject;
• receiving, by the trainer server, user sensory input via one or more sensors measuring activities and reactions of the user related to the presented data as the data is presented to the user;
• creating, by the trainer server, an attention map based on the user sensory input, the attention map being indicative of portions of the presented data on which the user focused and including one or more data classification regions;
• annotating, by the trainer server, the attention map with natural language input labels based on a natural language input of the user;
• training, by the trainer server, a neural network based on the annotated attention map, the training including communicating to a differential memory subsystem neural network information and processed user sensory input for experimental data processing functions;
• creating, by the trainer server, a model based on the trained neural network that is optimized for an anomalous activity of an end use application;
• providing, by the trainer server, an application program for an output target of the end use application;
• and
• instructing, by the trainer server, the output target via the application program to detect and remedy an anomalous activity.