Deep Learning for NLP: Neural Network Architectures

Natural language processing (NLP) is constantly evolving with cutting edge advancements in tools and approaches. Neural network architecture (NNA) supports this evolution by providing a method of processing language-based information to solve complex data-driven problems. Explore the basic NNAs relevant to NLP problems. Learn different challenges and use cases for single-layer perceptron multi-layer perceptron and RNNs. Analyze data and its distribution using pandas graphs and charts. Examine word vector representations using one-hot encodings Word2vec and GloVe and classify data using recurrent neural networks. After you have completed this course you will be able to use a product classification dataset to implement neural networks for NLP problems.