Get in Touch

Course Outline

Introduction to Deep Learning for NLP

Differentiating between various types of DL models

Using pre-trained vs. trained models

Leveraging word embeddings and sentiment analysis to extract meaning from text

Understanding how Unsupervised Deep Learning works

Installing and setting up Python Deep Learning libraries

Utilizing the Keras DL library atop TensorFlow to enable Python-generated captions

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

Using Keras on top of TensorFlow or Theano for rapid Deep Learning experimentation

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

Troubleshooting techniques

An overview of other specialized DL frameworks

Deploying your DL application

Accelerating DL using GPUs

Closing remarks

Requirements

  • Basic understanding of Python programming.
  • Familiarity with Python libraries in general.

Audience

  • Programmers interested in linguistics.
  • Developers seeking a deeper understanding of NLP (Natural Language Processing).
 28 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories