lava
Neuromorphic-Computing-Guide
| lava | Neuromorphic-Computing-Guide | |
|---|---|---|
| 3 | 10 | |
| 504 | 247 | |
| 2.6% | - | |
| 8.2 | 5.0 | |
| 7 days ago | 4 months ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | - |
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lava
- GitHub - lava-nc/lava: A Software Framework for Neuromorphic Computing
- Lava v0.4.0 just released, an open source software framework, community contributions encouraged
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Programmability of neuromorphic chips these days?
Although it looks like Intel is trying hard to bring in the community with their Lava Framework which th why say let’s you develop ‘neuro-inspired’ apps that map to a neurotrophic computer. Might be worth checking out tbh?
Neuromorphic-Computing-Guide
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I am extremely interested second language acquisition and Artificial intelligence. How can I get into research?
Start reading papers on https://www.biorxiv.org/ and notice what seems most interesting or promising to you. Learn python. There are actually quite a few open source "into to machine learning" courses - maybe start with MIT's Learning Library, see what you find there. I also have this bookmarked for myself for later; I'm sure there are a few more goodies worth checking out here: https://github.com/mikeroyal/Neuromorphic-Computing-Guide
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Getting Started with Neuromorphic Computing
Tools and Resources for getting started with Neumorphic Computing. The process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
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Neuromorphic Engineering
Neuromorphic engineering, which combines electrical, computer, and mechanical engineering with biology, physics, and neuroscience. uses specialized computing architectures that reflect the structure (morphology) of neural networks from the bottom up: dedicated processing units emulate the behavior of neurons directly in hardware, and a web of physical interconnections (bus-systems) facilitate the rapid exchange of information. Useful Tools and Resources for learning about Neuromorphic engineering.
- GitHub - mikeroyal/Neuromorphic-Computing-Guide: Neuromorphic Computing Guide
- Neuromorphic Computing that enables fast and power-efficient neural network–based artificial intelligence
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Neuromorphic Computing
Neuromorphic computing models the way the brain works through spiking neural networks and other types of neural networks. Useful Tools and Resources for learning about Neuromorphic Computing.
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Tools and Resources for Neuromorphic Computing
Useful Tools and Resources for learning about Neuromorphic Computing. Neuromorphic computing models the way the brain works through spiking neural networks and other types of neural networks.
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Tools and Resource for Neuromorphic Computing
UsefuleTools and Resource for about Neuromorphic Computing.
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Cool Neuromorphic Computing Guide/Wiki
Neuromorphic Computing Guide/Wiki: https://github.com/mikeroyal/Neuromorphic-Computing-Guide
What are some alternatives?
magicavoxel-shaders - A collection of shaders for MagicaVoxel to generate geometry, noise, patterns, and simplify common and repetitive tasks.
norse - Deep learning with spiking neural networks (SNNs) in PyTorch.
azure-sdk-for-python - This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.
Spiking-Neural-Network - Pure python implementation of SNN
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
python-performance - Repository for the book Fast Python - published by Manning
NIPY - Workflows and interfaces for neuroimaging packages
Shallow-learning - Replicating brain's low energy high efficiency model architecture & calculating (maths)
Pentest-Service-Enumeration - Suggests programs to run against services found during the enumeration phase of a Pentest
Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.