🔸Blockchain
🔸Decentralization
🔸Dapps
🔸Smart contracts
Resources to learn Web 3.0 for FREE
A thread🧵
This is an amazing course for anyone who wants to learn about the basics of blockchain technology and how it works.
https://t.co/Ru5vNrSmu0
🔸Bitcoin - https://t.co/ruhROny6h0
🔸Ethereum - https://t.co/tLkcYozfFy
🔸Solana - https://t.co/fGoMazTiEs
And similarly, you can find whitepapers on different blockchains easily on Google.
Developers can build their own web3 projects and earn NFT rewards on completion of the project.
@_buildspace
https://t.co/z0NupkTZ4i
It is a Web 3 education platform for developers for getting well versed with web 3 technology. It contains mostly all the different web 3 protocols
https://t.co/WW8TvQ67O4
It is a platform for hands-on experience in writing smart contracts in Solidity.
https://t.co/l4Xqqlkl25
Ethereum has a variety of Web3 tutorials from vetted Ethereum community members
https://t.co/S1zxfaoFbN
Learn through a series of quests that teaches the basics about Web3 with crypto and NFT as rewards. Each quest builds on what you learned in previous lessons, making it easy to learn and understand how all this new technology works together!
https://t.co/f2Nls9Gv4S
It is a hub for developers who want to learn about blockchain technology and how they can use it in their own projects
https://t.co/KKQohV1dXS
1. Follow me @meetjain74
2. Retweet the first tweet
3. Turn on the notifications to never miss these amazing tweets.
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More from All
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
