#mindtree
8 Sessions
28% Returns
Really fired like AK47 https://t.co/dSZim1iMJX
#mindtree
— Market Learner \U0001f3af (@MarketLearner7) August 11, 2021
AK-47 Firing Continuously
Again at Good Support before Start Firing back.
Disclaimer : Holding https://t.co/DKi16UNoOQ pic.twitter.com/KjgbhD7Re0
More from Market Learner 🎯
This Sector always rewarded with high Valuation by Mr. Market bcoz of must consumption business
Proxy for Housing and Infra Play
Charts attached - Price at Good Levels
@Vivek_Investor
@WealthEnrich
@caniravkaria https://t.co/ZJOxMx2J8S
Now Domestic Appliance Business
— Market Learner \U0001f3af (@MarketLearner7) June 13, 2021
A must consumption in Kitchen
Enjoying Duopoly in the business#TTK#Hawkins
Compared both players
Analyzed newly listed & Only listed Co in this business
Charts looking Great at CP @Vivek_Investor @vetris_stocks @Stockstudy8 @ca_mehtaravi https://t.co/kltgrigoml pic.twitter.com/VnbguRQd5X
Targets Achieved as mentioned in last tweet after pyramiding the trade.
Added upside targets if you are holding for longer time frame.
@Puretechnicals9
@ca_mehtaravi
@jainchintan67
Who bought this? https://t.co/Mu3P73Z5tR
Update Chart of #GujaratGas
— Market Learner \U0001f3af (@MarketLearner7) June 3, 2021
Shared Fibonacci view too
Hope you like It.
Already up 8% from last tweet.@Puretechnicals9 @rohanshah619 @ca_mehtaravi https://t.co/mLJlsjRA5w pic.twitter.com/GyREKtB53M
How Beautifully Price is giving respect to Trendline. https://t.co/55F37IKfKG
Time & Price Correction Came
— Market Learner \U0001f3af (@MarketLearner7) July 30, 2021
Taking supports as per mentioned levels &
Ready to bounce back
Multi-Bagger Giving Opportunity
Now its time to show execution power #LaurusLab #Saregama@jainchintan67 @RajarshitaS @ca_mehtaravi @nid_rockz https://t.co/95N2zTOCfr pic.twitter.com/jzhipRyBVs
More from All
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)