Deploying Deep Learning Models | Deep Learning for Engineers, Part 5

This video covers the additional work and considerations you need to think about once you have a deep neural network that can classify your data. We need to consider that the trained network is usually part of a larger system and it needs to be incorporated into that design. We also want to have some amount of confidence that the model will work on unseen data and that it’s going to interact as expected with the other system components. Ultimately, we also want to deploy it onto a target device which requires certain performance characteristics.

Check out these other links:

• MATLAB Deep learning examples: https://bit.ly/3deCj40​
• 5 Reasons to use MATLAB for deep learning: https://bit.ly/2QlbNNc
• Getting Started with Deep Network Designer: https://bit.ly/2Qof12l
• Quantization of Deep Neural Networks: https://bit.ly/3daOj6u
• Multi-Loop PI Control of a Robotic Arm: https://bit.ly/3sevqDS
• Deploying Deep Neural Networks to GPUs and CPUs using MATLAB Coder and GPU Coder: https://bit.ly/3tfhEST
• Deep Learning HDL Toolbox: https://bit.ly/3tl88xF

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