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Course Outline
Introduction to Deep Learning for NLP
Differentiating between the various types of DL models
Using pre-trained vs 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 allow Python to create captions
Working with Theano (numerical computation library) and TensorFlow (general and linguistics library) to use as extended DL libraries for the purpose of creating captions.
Using Keras on top of TensorFlow or Theano to quickly experiment on Deep Learning
Creating a simple Deep Learning application in TensorFlow to add captions to a collection of pictures
Troubleshooting
A word on other (specialized) DL frameworks
Deploying your DL application
Using GPUs to accelerate DL
Closing remarks
Requirements
- An understanding of Python programming
- An understanding of Python libraries in general
Audience
- Programmers with interest in linguistics
- Programmers who seek an understanding of NLP (Natural Language Processing)
28 Hours
Testimonials (2)
Exercises and exchanges during questions/answers
Antoine - Physiobotic
Course - Scaling Data Pipelines with Spark NLP
Machine Translated
I liked the opportunities to ask questions and get more in depth explanations of the theory.
