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Leveraging Deep Learning for Natural Language Processing Course

Course 1278

  • Duration: 3 days
  • Labs: Yes
  • Language: English
  • Level: Intermediate
Get This Course 23.000 kr
  • 3-day instructor led training course

  • After-course coaching available

#1278
  • jul 19 - 21 10:00 - 17:30 CEST
    AnyWare
  • sep 7 - 9 15:00 - 22:30 CEST
    New York or AnyWare
  • sep 27 - 29 10:00 - 17:30 CEST
    AnyWare
  • nov 21 - 23 15:00 - 22:30 CET
    Ottawa or AnyWare
  • nov 29 - dec 1 10:00 - 17:30 CET
    AnyWare
  • jan 31 - feb 2 10:00 - 17:30 CET
    AnyWare
  • mar 28 - 30 10:00 - 17:30 CEST
    AnyWare
  • mar 29 - 31 15:00 - 22:30 CEST
    New York or AnyWare

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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.

Natural Language Processing Course Delivery Methods

  • In-Person

  • Online

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 recogniser 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

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

In this module, you will learn about: 

  • Named Entity Recognition and how to develop it using popular libraries
  • Parts of Speech Tagging

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)
  • In this module, you will learn about CNN architecture, application areas, and implementation using Keras.
  • In this module, you will learn about RNN architecture, application areas, vanishing gradients, and implementation using Keras.
  • In this module, you will learn about GRU architecture, application areas, and implementation using Keras.
  • In this module, you will learn about LSTM architecture, application areas, and implementation using Keras.

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

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)

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Natural Language Processing Training FAQs

To succeed in this course, you must have a strong working knowledge of Introduction to Python Training. We also recommend some knowledge of linear algebra and machine learning.

You will get a better understanding of the algorithms used in natural language processing if you first do one of the following:

More and more, the source of data for AI is in the form of human text or voice. With this course, you will gain the necessary skills to transform this data into numbers that can be used to gain insights from such data. This would be for understanding, for instance, chatbots or sentiment analysis, classification of documents, search, etc.

Yes! Our Leveraging Deep Learning for Natural Language Processing course builds on these courses and uses Jupyter and Keras to build models for natural language processing.