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Deep Learning for Natural Language Processing

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 Deep Learning for Natural Language Processing Course:

  • After-course instructor coaching benefit
  • After-course computing sandbox included
  • End-of-course exam included

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.

Select specific date to see price, venue and full details.

Learning Objectives

  • 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


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

Course Content

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 organisation

  • 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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