Leveraging Deep Learning for Natural Language Processing Course

In this Natural Language Processing course, you will learn how to navigate the various text pre-processing techniques and select the best neural network architecture for Natural Language Processing. Leveraging Deep Learning for Natural Language Processing Course Benefits In this course, you learn how to:  Understand various pre-processing techniques for deep learning problems. Build a vector representation of text using word2vec and GloVe. Create a named entity recognizer and parts-of-speech tagger with Apache OpenNLP. Build a machine translation model in Keras, a deep learning API. Develop a text Career Stages application using Long short-term memory (LSTM). Build a trigger word detection application using an attention model. Test your knowledge in the included end-of-course exam. Continue learning and face new challenges with after-course one-on-one instructor coaching. Natural Language Processing Course Outline Module 1: Introduction to Natural Language Processing In this module, you will learn about: The basics of Natural Language Processing and its applications Popular text pre-processing techniques Word2vec and Glove word embeddings Sentiment classification Module 2: Applications of Natural Language Processing In this module, you will learn about:  Named Entity Recognition and how to develop it using popular libraries Parts of Speech Tagging Module 3: Introduction to Neural Networks In this module, you will learn about: Basics of Gradient descent and backpropagation. Fundamentals of Deep Learning, Keras and deploying a Model-as-a-Service (MaaS) Module 4: Foundations of Convolutional Neural Networks (CNN) In this module, you will learn about CNN architecture, application areas, and implementation using Keras. Module 5: Recurrent Neural Networks (RNN) In this module, you will learn about RNN architecture, application areas, vanishing gradients, and implementation using Keras. Module 6: Gated Recurrent Units (GRU) In this module, you will learn about GRU architecture, application areas, and implementation using Keras. Module 7: Long Short-Term Memory (LSTM) In this module, you will learn about LSTM architecture, application areas, and implementation using Keras. Module 8: State of the Art in Natural Language Processing In this module, you will learn how to: Perform Attention Model and Beam search Use End to End models for speech processing Use Dynamic Neural Networks to answer questions Module 9: A Practical NLP Project Workflow in an Organization In this module, you will learn how to: Acquire data using free datasets and crowdsourcing Use cloud infrastructure, such as the Google collab notebook, to train deep learning NLP models Write a Flask framework server RestAPI to deploy a model Deploy the web service on cloud infrastructures such as Amazon Elastic Compute Cloud (Amazon EC2) or Docker Cloud Leverage the promising techniques in NLP, such as Bidirectional Encoder Representations from Transformers (BERT)