- Increasing the accuracy of a model with parameter tuning.
- Converting models to C or CUDA to take advantage of speed and hardware.
- Experimenting with new network architectures for transfer learning.
Trend #1: Cloud Computing We all know training complicated networks takes time. Adding to that, techniques like Bayesian optimization - which will train your network multiple times with different training parameters – can provide powerful results at a cost: more time. An option to alleviate some of this pain is to move from local resources to clusters (HPC) or the cloud. The cloud is emerging as a great resource: providing the latest hardware, multiple GPUs at one time, and only paying for resources when they are needed. How MATLAB helps you with this trend:
- Check out the MATLAB cloud computing page.
- And MATLAB specific NVIDIA GPU Cloud (NGC) support in our documentation.
- There's also a walk-through video on how to set up MATLAB and NGC here.
- GPU Coder is the product to watch. It was named Embedded Vision’s 2018 product of the year.
- MATLAB has coder tools and support packages to various devices: including iOS, Android, and FPGA to name a few
- Though not deep learning specific, I've heard App Designer now supports Web App deployment.
*Introductions: As Steve mentioned in his last post, I’ll be taking over the blog, and I’m very excited for this new challenge! For those interested, I want to introduce myself and talk about my vision for this blog. As you may have seen, I’ve been warming up for this role as a guest blogger writing about "deep learning in action" (part 1, part 2 and part 3) And prior to that, I took over the ‘pick of the week’ blog and wrote about our deep learning tutorial series. I maintain the MATLAB for Deep Learning content, and I appear in a few videos on our site from time to time. Background: I've been at MathWorks 5 years. I started as an Application Engineer, which meant I got to travel to customer sites and present to customers, specializing in image processing and computer vision. My theory is most of you reading this have never experienced a MathWorks seminar, and I’d like that to change. I now work in marketing**: My current job is making sure everyone knows about the capabilities of the tools and how to solve their problems, and this blog fits well within this job description. **Some people shudder when they hear the word marketing. It’s still a technical role, I just happen to also like spending time on better wording, formatting and visualizations. It’s a win-win for you. You’ll see! Blog Vision: My vision for this blog in one word is access. I have access to insider information because I work here. My goal for this blog is to be the source of that information. I want to talk about the behind-the-scenes deep learning things that you may not see by reading the documentation. While I can’t share future plans, I can give insight, demos, developer Q&A time, and code you won’t find in the product. That’s the vision. I hope you’ll join me on this journey!
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