Presentation: Neural Networks Across Space and Time
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What You’ll Learn
- Hear what neural networks are and what types of deep learning networks are out there.
- Learn how to decide if your solution needs a convolutional network or a recurrent one.
- Find out what some of the DL resources are out there and see how some are used.
Abstract
This is an intro-level talk on deep learning. We’ll start with a brief introduction to deep neural networks, why they are important and how they work. The talk will then cover two of the most important deep neural architectures: convolutional networks which excel at handling images and recurrent networks which handle time-series or sequential input (such as text). We'll show examples of both convolutional and recurrent networks using the deeplearning4j framework.
What is the focus of your work today?
I’m one of the lead developers of the management layer for Horizon Cloud; VMware’s cloud-hosted virtual desktop product
What’s the motivation for this talk?
Andre Karpathy has described neural networks as "Software 2.0" Even if he's overestimating, machine learning is only going to get more important. Developers wanting to stay ahead should at least gain a basic understanding of what's involved and how it can be used.
How you you describe the persona and level of the target audience?
A developer who's heard some of the hype about deep learning and wants to know what deep learning actually is. No prior knowledge of machine learning is assumed.
What do you want this persona to walk away from your talk knowing that they might not have known 50 minutes before?
A basic understanding of what a neural network is and how it works. The two predominant deep network architectures today, what they are used for and how to build them.
This talk is in the Java track. Do you think this talk would be interesting to non-java developers as well?
Yes, because although I'm going to use Java examples, a lot of frameworks out there have broadly similar primitives.
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