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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.

You Will Learn How To

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Key Features of this Deep Learning for Natural Language Processing Course:

  • 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

Choose the Training Solution That Best Fits Your Individual Needs or Organizational Goals

LIVE, INSTRUCTOR-LED

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 $2650

Government $2355

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Note: This course runs for 3 Days *

*Events with the Partial Day Event clock icon run longer than normal but provide the convenience of half-day sessions.

  • Nov 4 - 6 9:00 AM - 4:30 PM EST Online (AnyWare) Online (AnyWare) Reserve Your Seat

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  • Feb 3 - 5 9:00 AM - 4:30 PM EST Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

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Learning Tree offers a flexible schedule program. If you cannot attend full day sessions, this option consists of four-hour sessions per day instead of the full-day session.

Important Deep Learning for Natural Language Processing Course Information

  • Requirements

    Strong working knowledge of Python, linear algebra, and machine learning is a must.

  • 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 book for you.

Deep Learning for Natural Language Processing Course 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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Deep Learning for Natural Language Processing FAQs

  • Can I take this data science course online?

    Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.

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