Unleash the Power of Deep Learning with Transfer Learning

Transfer learning is a machine learning \ deep learning technique where knowledge gained during training in one set of machine learning problem can be used to train other similar types of problems. This is an extremely useful approach to leveraging pre-trained models to solve real-world problems having constraints and limitations of less data availability.

This talk will cover essentials around deep learning and transfer learning concepts. The various methodologies of transfer learning. We will then look at diverse ways of how transfer learning can be applied in the real-world on complex problems around the following areas.

  • Computer Vision
  • Natural Language Processing
  • Audio Categorization

We will briefly look at a multitude of real-world case studies and problems around the preceding areas like text classification, image classification, image captioning, style transfer and audio classification.

 
 

Outline/Structure of the Talk

  1. Brief into Machine Learning & Deep Learning
  2. What is Transfer Learning
  3. Strategies for Transfer Learning
  4. The power of Transfer Learning - solving a small data constraint problem
  5. Introduction to pre-trained models for computer vision - VGG, Inception etc
  6. Transfer Learning case-studies for computer vision
    1. Image Classification
    2. Style Transfer
  7. Transfer Learning case-studies for audio
    1. Audio Classification
  8. Transfer Learning case-studies for text
    1. Text Classification
    2. Image Captioning
  9. Conclusion & Wrap-up

Learning Outcome

  • Learn essential concepts pertaining to deep learning and transfer learning
  • Learn about effective strategies for transfer learning
  • Get to know real-world applications of transfer learning
  • Get an in-depth perspective of complex problems and possible solutions in computer vision, audio and text.

Target Audience

Data Scientists, Data Enthusiasts & Anyone interested in Deep Learning

Prerequisites for Attendees

Knowing basics around machine learning (types) and deep learning concepts (layers, architectures) helps. However the talk will cover these areas briefly before we get started, so anyone should still be able to follow.

schedule Submitted 1 year ago

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