Issue link: https://resources.mouser.com/i/1437726
10 REIMAGINING WHAT'S NEXT For both testing and training, we have 1,000 questions each. However, we have not used this much data as it might not be of much use. The results of this model were a testing accuracy of 99.6 percent, training accuracy of 97.6 percent, and validation accuracy of 88 percent. TensorFlow framework has shown good results for training neural network models with NLP models showing good accuracy. Training, testing and loss results have been great. Langmod_nn model and memory networks resulted in good accuracy rates with low loss and error value. The flexibility of the memory model allows it to be applied to tasks as diverse as question answering and to language modeling. Conclusion As shown, NLP provides a wide set of techniques and tools that can be applied in all areas of life. By learning the models and using them in everyday interactions, quality of life would highly improve. NLP techniques help to improve communications, reach goals, and improve the outcomes received from every interaction. NLP helps people to use the tools and techniques that are already available to them. By learning NLP techniques properly, people can achieve goals and overcome obstacles. In the future, NLP will move beyond both statistical and rule-based systems to a natural understanding of language. Some improvements have already been made by tech giants. For example, Facebook tried to use deep learning to understand text without parsing, tags, named-entity recognition (NER), etc., and Google is trying to convert language into mathematical expressions. Endpoint detection using grid long short- term memory (LSTM) networks and end-to-end memory networks on bAbI task performed by Google and Facebook, respectively, shows the advancement that can be done in NLP models. Learn More INTEL® XEON® 3RD GENERATION SCALABLE PROCESSORS