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Course Outline

Introduction to Deep Learning for NLP

Distinguishing between various types of DL models

Using pre-trained versus trained models

Using word embeddings and sentiment analysis to extract meaning from text

How Unsupervised Deep Learning works

Installing and setting up Python Deep Learning libraries

Using the Keras DL library on top of TensorFlow to enable Python to generate captions

Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) as extended DL libraries for caption generation

Using Keras on top of TensorFlow or Theano to rapidly experiment with Deep Learning

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

Troubleshooting

A note on other (specialised) DL frameworks

Deploying your DL application

Using GPUs to accelerate Deep Learning

Closing remarks

Requirements

  • Understanding of Python programming
  • Familiarity with Python libraries in general

Audience

  • Programmers with an interest in linguistics
  • Programmers seeking to understand NLP (Natural Language Processing)
 28 Hours

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