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Norse Alternatives
Similar projects and alternatives to norse
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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spikingjelly
SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Neuromorphic-Computing-Guide
Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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RWKV-LM
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
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SpikeGPT
Implementation of "SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks"
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notebooks
Notebooks illustrating the use of Norse, a library for deep-learning with spiking neural networks. (by norse)
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DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
norse reviews and mentions
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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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[D] The Complete Guide to Spiking Neural Networks
Surrogate gradients and BPTT, this is what is implemented in Norse https://github.com/Norse/Norse. It is also possible to compute exact gradients using the Eventprop algorithm.
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Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
That repo is slightly outdated, development now continues at https://github.com/norse/norse.
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A note from our sponsor - WorkOS
workos.com | 29 Mar 2024
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norse/norse is an open source project licensed under GNU Lesser General Public License v3.0 only which is an OSI approved license.
The primary programming language of norse is Python.