Applications of deep learning: overview

In this course, you will learn how deep learning works

Medical industry: How deep learning has taken medical imaging and drug discovery to new productivity levels. Deep learning algorithms can read medical images as good as doctors can.

Object recognition: Finding and tagging objects in an image is a mostly 'solved' problem. 

Question-answering: Deep nets enable conversational interfaces. You can book a meeting or a flight over chat without ever noticing that the customer representative was a bot. Voice recognition (Siri, Google Now, Cortana) works well enough to be deployed in products.

Car manufacturing industry: Beyond self-driving cars, there are quite a few use cases where deep nets can make a useful contribution.‍

After attending this course you should know

  • Why deep nets are getting so much attention in the machine learning community
  • What is the best hardware configuration for training deep nets
  • What are the best practices to prevent overfitting
  • How not to reinvent wheels by using the best existing implementations, and how to tell them apart
  • A few algorithms in depth (RNN, convnets)
  • What pretrained networks are available, and for which problems they are a great starting point
  • What are applications of deep nets beyond computer vision