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Leveraging Deep Learning for Natural Language Processing Course
Course 1278
3 DAY COURSE

Price:
Course Outline

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

  • 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 generation 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)
Course Dates

For course questions or any customer service inquiry, please contact your Customer Service team at BAHCustomerService@LearningTree.com.

We are excited that Learning Tree now offers a deferred direct bill payment option for Booz Allen employees. The deferred direct bill payment option enables employees to enroll in learning opportunities with no upfront costs. This payment option will require the employee to sign a Family Educational Rights and Privacy Act (FERPA) agreement with Learning Tree to release grades/completion to Booz Allen to satisfy the FlexEd Program completion requirement. Note, Learning Tree may also be used for the FlexEd Program reimbursement payment option.

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Private Team Training

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