Deep Learning for NLP (Natural Language Processing) Training Course
Deep Learning (DL) represents a specialized branch of Machine Learning (ML).
Python, a widely adopted programming language, offers extensive libraries tailored for Deep Learning in the context of Natural Language Processing (NLP).
Through Deep Learning for NLP, machines can acquire the ability to process language ranging from basic to highly complex structures. Current applications include machine translation and automatic image captioning.
During this instructor-led live training, attendees will explore Python libraries for NLP while developing an application capable of processing a series of images to generate corresponding captions.
Upon completion of this training, participants will be able to:
- Design and implement Deep Learning for NLP solutions using Python libraries.
- Write Python code to analyze vast collections of images and extract relevant keywords.
- Develop Python code that generates descriptive captions based on the detected keywords.
Course Format
- A combination of lectures, discussions, exercises, and intensive hands-on practice.
Course Outline
Introduction to Deep Learning for NLP
Distinguishing between various types of DL models
Comparing pre-trained models with custom-trained models
Utilizing word embeddings and sentiment analysis to derive meaning from text
Understanding the mechanics of Unsupervised Deep Learning
Installing and configuring Python Deep Learning libraries
Leveraging the Keras DL library built on 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 frameworks for caption generation.
Employing Keras on top of TensorFlow or Theano for rapid Deep Learning experimentation
Developing a simple Deep Learning application in TensorFlow to add captions to an image dataset
Troubleshooting common issues
Overview of other specialized DL frameworks
Deploying your DL application
Utilizing GPUs to accelerate Deep Learning processes
Closing remarks
Requirements
- Proficiency in Python programming.
- General understanding of Python libraries.
Target Audience
- Programmers with an interest in linguistics.
- Developers seeking to understand Natural Language Processing (NLP).
Open Training Courses require 5+ participants.
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