DEEP NATURAL LANGUAGE PROCESSING IMPLEMENTATION USING TENSORFLOW

Machine Comprehension is a very interesting task in both natural language processing and artificial intelligent research but extremely challenging.There are several approaches to NLP tasks in general. With recent breakthroughs allowed in algorithms (deep learning), hardware (GPUs) and user friendly APIs (TensorFlow), some tasks have become feasible up to a certain accuracy. This project report contains TensorFlow implementations of various deep learning models, with a focus on problems in Natural Language Processing. Given the following models implementation and training which are completed in the project done at NIT Srinagar using Intel DevCloud and optimized Intel TensorFlow Framework: 1. Mnist_cnn: A three-layer Convolutional Neural Network for the MNIST Handwritten Digit Classification task. The architecture is a form of Memory Network but unlike the model in that work, it is trained end-to-end, 4. Variational_autoencoder: Variational Autoencoder for the MNIST Handwritten Digits dataset.