Discover and read the best of Twitter Threads about #datavalidation

Most recents (3)

$DAG Nodes are the workhorse of the entire @Conste11ation HGTP ecosystem from L1 networks (dApps) to Global validation nodes.
Next few days I'll cover each different type:
L1 nodes
Hybrid nodes
Validator nodes

Today I'm going to start with L1 or dApps building on the network πŸ‘‡
🧡1/ (L1) Nodes πŸŒπŸ’»: A deep dive into the unique nodes within individual L1 Networks, their role, and how they contribute to the overall Hypergraph ecosystem.
#L1Nodes #DecentralizedNetwork
2/ L1 nodes are specific to their respective Networks, allowing them to support a wide range of applications and use cases. They differ from Global Validator Nodes, which focus on maintaining the global network.
#Networks #Scalability
Read 10 tweets
1/10 🌐 Curious about why you would use @Conste11ation $DAG when there are specialized blockchain solutions like @energywebx $EWT and @MNWSupplyChain $MNW? Let's explore how Constellation can complement and enhance these networks! #Constellation #Interoperability
2/10 ⚑ @energywebx $EWT is focused on building a decentralized energy market, and @Conste11ation $DAG can provide secure data pipelines and validation for IoT devices in the energy sector, enhancing data integrity and performance. #EnergyWeb #IoT
3/10 πŸ”„ By integrating with @Conste11ation, @energywebx can benefit from the scalable and secure HyperGraph network, allowing for seamless data exchange between different energy market players and fostering innovation. #HyperGraph #EnergyMarket
Read 10 tweets
✨🧠 The ecosystem that has grown up around @TensorFlow in the last few years blows my mind. There's just so much functionality, compared to some of the other, newer frameworks.

πŸ‘‰Consider this an ever-expanding thread for me to take notes + wrap my brain around products. Ready?
1) @TensorFlow Extended (TFX)

It's no secret that I πŸ’• #TFX and all of its tooling for deploying machine learning models into production. If you care about keeping your models up-to-date and monitoring them, you should check out the product + its paper.

tensorflow.org/tfx/?hl=zh-cn
2) @TensorFlow Hub

If you want to train your model on a small data set, or improve generalization, you'll need to use something called transfer learning. #TFHub modules make it easyβ€”and are available in an #OSS marketplace: tfhub.dev.

site: tensorflow.org/hub/
Read 40 tweets

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