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