Deep Learning for NLP (Natural Language Processing) Training Course
Deep Learning (DL) represents a specialized subset of Machine Learning (ML).
Python has emerged as a highly popular programming language, offering robust libraries designed for Deep Learning in the context of Natural Language Processing (NLP).
Through Deep Learning for NLP, machines gain the ability to process language, ranging from simple to complex structures. Current applications include language translation and the automatic generation of captions for images.
In this instructor-led live training, participants will learn to utilize Python libraries for NLP by building an application that processes a set of images and generates corresponding captions.
Upon completion of this training, participants will be capable of:
- Designing and coding Deep Learning solutions for NLP using Python libraries.
- Writing Python code that ingests large volumes of images and extracts relevant keywords.
- Developing Python code that produces captions based on the identified keywords.
Course Format
- A blend of lectures, discussions, exercises, and extensive hands-on practice
Course Outline
Introduction to Deep Learning for NLP
Differentiating between various types of DL models
Utilizing pre-trained versus custom-trained models
Extracting meaning from text using word embeddings and sentiment analysis
Understanding how Unsupervised Deep Learning functions
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-purpose and linguistic library) as extended DL libraries for caption generation
Rapidly experimenting with Deep Learning by using Keras on top of TensorFlow or Theano
Building a simple Deep Learning application in TensorFlow to add captions to a collection of images
Troubleshooting techniques
Overview of other specialized DL frameworks
Deploying your DL application
Accelerating DL using GPUs
Closing remarks
Requirements
- Foundational knowledge of Python programming
- General understanding of Python libraries
Audience
- Programmers with an interest in linguistics
- Programmers seeking to understand Natural Language Processing (NLP)
Open Training Courses require 5+ participants.
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