notebooks
NYU-DLSP20
| notebooks | NYU-DLSP20 | |
|---|---|---|
| 2 | 2 | |
| 24 | 6,625 | |
| - | - | |
| 0.0 | 6.1 | |
| over 1 year ago | 4 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | GNU General Public License v3.0 or later |
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notebooks
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Neuromorphic learning, working memory, and metaplasticity in nanowire networks
This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...
Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.
Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.
I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?
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Event-Based Backpropagation for Exact Gradients in Spiking Neural Networks
We've written some documentation around our neuron equations in Python that explains this: https://norse.github.io/norse/auto_api/norse.torch.functiona...
See also our tutorial on neuron parameter optimization to understand how it's useful for machine learning: https://github.com/norse/notebooks#level-intermediate
Disclaimer: I'm a co-author of the library Norse
Regarding the target audience, it's actually not entirely clear to me. This lies in the intersection between computational neuroscience and deep learning. Which isn't a huge set of people. Meaning, you're questions are valid and we (as researchers) have a lot of communication to do to explain why this is interesting and important.
NYU-DLSP20
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A collection of some of the best PyTorch courses for beginners to learn PyTorch online
And of course our NYU DL course 😉 https://github.com/Atcold/pytorch-Deep-Learning
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Week 6 practicum notebook
I am going through week 6 practicum notebook. Can someone shed some light on the following code in train method:
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