Leveraging Deep Learning for Natural Language Processing

Level: Intermediate

Starting with the basics, this course teaches you how to choose from the various text pre- processing techniques and select the best model from the several neural network architectures for NLP issues.

Key Features of this Leveraging Deep Learning for Natural Language Processing Training:

  • After-course instructor coaching benefit
  • After-course computing sandbox included
  • Learning Tree end-of-course exam included

You Will 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
  • Develop a text generation application using LSTM
  • Build a trigger word detection application using an attention model

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In Class & Live, Online Training

  • 3-day instructor led training course
  • One-on-one after-course instructor coaching
  • Pay later by invoice -OR- at the time of checkout by credit card
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Standard $2745 CAD

Government $2415 CAD




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In Class & Live, Online Training

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Note: This course runs for 3 Days

  • Mar 23 - 25 9:00 AM - 4:30 PM EDT New York / Online (AnyWare) New York / Online (AnyWare) Reserve Your Seat

  • May 25 - 27 9:00 AM - 4:30 PM EDT Ottawa / Online (AnyWare) Ottawa / Online (AnyWare) Reserve Your Seat

  • Sep 7 - 9 9:00 AM - 4:30 PM EDT New York / Online (AnyWare) New York / Online (AnyWare) Reserve Your Seat

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Leveraging Deep Learning for Natural Language Processing Training Information

  • Requirements

    Strong working knowledge of Python is a must. Knowledge of linear algebra and machine learning is an advantage, but not required.

  • Who Should Attend This Course

    If you’re an aspiring data scientist looking for an introduction to deep learning in the NLP domain, this is just the course for you.

    Leveraging Deep Learning for Natural Language Processing Training Outline

    • Lesson 1: Introduction to Natural Language Processing

      • Basics of Natural Language Processing & application areas.
      • Introduction to popular text pre-processing techniques.
      • Introduction to word2vec and Glove word embeddings.
      • Sentiment classification.
    • Lesson 2: Applications of Natural Language Processing

      • Introduction to Named Entity Recognition.
      • Introduction to Parts of Speech Tagging.
      • Using popular libraries to develop a Named Entity Recognizer.
    • Lesson 3: Introduction to Neural Networks

      • Introduction to Neural Networks.
      • Basics of Gradient descent and backpropagation.
      • What is Deep Learning.
      • Introduction to Keras.
      • Fundamentals of deploying a model as a service.
    • Lesson 4: Foundations of Convolutional Neural Networks

      • Introduction to CNN.
      • Understanding the architecture of a CNN.
      • Application areas of a CNN.
      • Implementation using Keras.
    • Lesson 5: Recurrent Neural Networks

      • Introduction to RNN.
      • Understanding the architecture of a RNN.
      • Application areas of a RNN.
      • Implementation using Keras.
      • Vanishing Gradients with RNN.
    • Lesson 6: Gated Recurrent Units

      • Introduction to GRU.
      • Understanding the architecture of a GRU.
      • Application areas.
      • Implementation using Keras.
    • Lesson 7: Long Short Term Memory

      • Introduction to LSTM.
      • Understanding the architecture of an LSTM.
      • Application areas.
      • Implementation using Keras.
    • Lesson 8: State of the art in Natural Language Processing

      • Attention Model & Beam search.
      • End to End models for speech processing.
      • Dynamic Neural Networks for question answering.
    • Lesson 9: A practical NLP project workflow in an organization

      • Data acquisition (Free datasets, crowd-sourcing).
      • Using cloud infrastructure to train deep learning NLP model (Google colab notebook).
      • Writing a Flask framework server RestAPI to deploy a model.
      • Deploy the web service on cloud infrastructure (AWS ec2 instance, docker).
      • Current promising techniques in NLP (BERT and others).

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