Today work starts on my next book - KINGS & QUEENS. It will include all English monarchs since Alfred the Great in 886AD, of which there are 61 + Oliver Cromwell, who will also get a chapter. I've already started recruiting writers, but am open to suggestions.

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Master Thread of all my threads!

Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃

1. 7 FREE OPTION TRADING COURSES FOR


2. THE ABSOLUTE BEST 15 SCANNERS EXPERTS ARE USING

Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX


3. 12 TRADING SETUPS which experts are using.

These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
3. @ITRADE191
4.


4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
1. Management
2. How to initiate
3. When to exit straddles
4. Examples
5. Videos on
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)

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