Official website: Aquila Network - Decentralized Content Discovery Network
A curated list of Aquila Network official repositories, client libraries, ecosystem projects, boilerplates, tutorials, learning and more..
If docker
and docker-compose
are installed in your machine, use below command to setup everything:
wget -O - https://raw.githubusercontent.com/Aquila-Network/AquilaX-CE/main/setup_aquilax.sh | /bin/bash
for more details on installation, check Aquila X CE repository
- Install convenient browser extensions (Chrome, Firefox), check Aquila X browser extensions
- Still struggling to setup? Watch tutorial videos
Aquila Network at it's core is a common set of protocols to be followed to join and behave in the network of nodes. Anybody can follow these specifications to implement different components in Aquila Network ecosystem and participate in the network. Below are the essential documents.
Here is a bird's eye view of every component that fit into Aquila Network
Official implementation of different components in Aquila Network maintained by Aquila Network core team.
Aquila DB - Data storage and management component
Aquila Port - Network protocols and replication component
Aquila Hub - Knowledge compression component (ML models management)
Aquila X - Aquila Network exploration component
[CLI]
Source code - Source code of AquilaX browser extensions all in one place
- Supports all chromium variants such as Chrome, Chromium, Brave, Edge etc.
- Supports all Firefox variants
Unofficial implementation of different components in Aquila Network maintained by community.
[submit pull request]
[submit pull request]
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paper: https://www.microsoft.com/en-us/research/uploads/prod/2017/06/INR-061-Mitra-neuralir-intro.pdf
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Embeddings for Everything: Search in the Neural Network Era: https://www.youtube.com/watch?v=JGHVJXP9NHw
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Autoencoders are one such deep learning algorithms that will help you to build semantic vectors - foundation for Neural Information retrieval. Here are some links to Autoencoders based IR:
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Hutter prize for compressing human knowledge
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Rationale for a Large Text Compression Benchmark and Benchmark results
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Facebook Research MARGE
Contributions welcome! Read the contribution guidelines first.