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Course Outline

Introduction to Deep Learning for NLP

Differentiating between various types of DL models

Comparing pre-trained models with custom-trained models

Utilizing word embeddings and sentiment analysis to extract meaning from text

Understanding how Unsupervised Deep Learning functions

Installing and configuring Python Deep Learning libraries

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

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

Rapid experimentation with Deep Learning using Keras atop TensorFlow or Theano

Building a simple Deep Learning application in TensorFlow to add captions to an image collection

Troubleshooting common issues

Overview of other specialized DL frameworks

Deploying your DL application

Leveraging GPUs to accelerate Deep Learning processes

Closing remarks

Requirements

  • Foundational knowledge of Python programming
  • General understanding of Python libraries

Target Audience

  • Programmers interested in linguistics
  • Developers seeking to understand Natural Language Processing (NLP)
 28 Hours

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