Fundamentals of Deep Learning for Natural Language Processing | SGInnovate



Fundamentals of Deep Learning for Natural Language Processing

Presented by SGInnovate, NVIDIA & NSCC

SGInnovate, together with the NVIDIA Deep Learning Institute (DLI) is proud to bring to you Fundamentals of Deep Learning for Natural Language Processing.

Led by experienced instructors, you can expect to learn the latest deep learning techniques for designing and deploying neural network-powered machine learning across a variety of application domains.

In the course, participants will learn to:

  • Implement common deep learning workflows such as image segmentation and text generation
  • Compare and contrast data types, workflows, and frameworks
  • Combine deep learning powered computer vision and natural language processing to start
  • Solve sophisticated real-world problems that require multiple input data types

This workshop teaches you to apply deep learning techniques for understanding textual input using Natural Language Processing (NLP) through a series of hands-on exercises. You will work with widely-used deep learning tools, frameworks, and workflows by performing neural network training on a fully-configured GPU accelerated workstation in the cloud. 

The course starts with the technique of training a neural network for text classification followed by building a linguistic style model to extract features from a given text document and concludes with a neural machine translation model for translating one language to another. 

You should also have basic experience with Neural Networks and python programming and/or familiarity with linguistics.

08:45am – 09:00am: Registration
09:00am – 09:45am: Introduction
Introduction to deep learning, situations in which it is useful, key terminology, industry trends and challenges

  • Course Overview
  • Getting Started with Deep Learning

09:45am – 10:00am: Tea Break
10:00am – 12:00pm: Image Segmentation with TensorFlow
Hands-on exercise: Segment MRI images to measure parts of the heart using tools such as TensorBoard and the TensorFlow Python API

  • Compare image segmentation to other computer vision problems
  • Experiment with TensorFlow tools
  • Implement effective metrics for assessing model performance

12:00pm – 1:00pm: Lunch
1:00pm – 3:00pm: Word Generation with TensorFlow
Hands-on exercise: Train a Recurrent Neural Network to understand both images and text, and to predict the next word of a sentence using the MSCOCO (Microsoft Common Objects in Context) dataset 

  • Introduction to Natural Language Processing (NLP) and Recurrent Neural Networks (RNNs)
  • Create network inputs from text data
  • Test with new data
  • Iterate to improve performance

3:00pm – 3:15pm: Tea Break
3:15pm – 5:15pm: Image and Video Captioning
Hands-on exercise: Train a model that generates a description of an image from raw pixel data by combining outputs of multiple networks (CNNs and RNNs) through concatenation and/or averaging.

  • Combine computer vision and natural language processing to describe scenes
  • Learn to harness the functionality of Convolutional Neural Networks (CNNs) and RNNs

5:15pm – 5:30pm: Summary
Review of concepts and practical takeaways

  • Summary of key learnings 
  • Workshop survey

For group discounts or enquiries, please contact [email protected].

Topics: Artificial Intelligence / Deep Learning / Machine Learning / Robotics

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