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Course Outline

Introduction to Deep Learning for NLP

Differentiating between various types of DL models

Utilizing pre-trained versus custom-trained models

Employing word embeddings and sentiment analysis to extract meaning from text

Understanding how Unsupervised Deep Learning works

Installing and setting up Python Deep Learning libraries

Using the Keras DL library atop TensorFlow to enable Python to generate captions

Working with Theano (a numerical computation library) and TensorFlow (a general and linguistic library) to utilize them as extended DL libraries for caption creation.

Utilizing Keras atop TensorFlow or Theano for rapid experimentation in Deep Learning

Building a simple Deep Learning application in TensorFlow to add captions to a collection of images

Troubleshooting

A note on other (specialized) DL frameworks

Deploying your DL application

Leveraging GPUs to accelerate DL

Closing remarks

Requirements

  • A fundamental understanding of Python programming
  • A general understanding of Python libraries

Audience

  • Programmers with an interest in linguistics
  • Programmers seeking a deeper understanding of NLP (Natural Language Processing)
 28 Hours

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