![](https://pbs.twimg.com/media/Fb2_g4CUYAEcGME.png)
Top 10 Python program for daily basis work.
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More from Python Coding
Top 10 Python Courses from Eduonix.
🧵:
1. Learn Python Fundamentals https://t.co/xv9ZlCfILS
2. Learn Python Basic to Advance in Easy Way 2022 https://t.co/bKtl1QtMV3
3. Python for Beginners. Learn Twice (FREE Presentation EBOOK) https://t.co/owclho2ss9
4. Data Visualization in python using seaborn library https://t.co/RLkKp9zlVf
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1. Learn Python Fundamentals https://t.co/xv9ZlCfILS
![](https://pbs.twimg.com/media/Ff_P5INUAAAfvtK.png)
2. Learn Python Basic to Advance in Easy Way 2022 https://t.co/bKtl1QtMV3
![](https://pbs.twimg.com/media/Ff_QV-OUAAAAqX4.png)
3. Python for Beginners. Learn Twice (FREE Presentation EBOOK) https://t.co/owclho2ss9
![](https://pbs.twimg.com/media/Ff_Qmj2VQAUlUJS.png)
4. Data Visualization in python using seaborn library https://t.co/RLkKp9zlVf
![](https://pbs.twimg.com/media/Ff_Q1XtVUAEQOoo.png)
Free Python PDF Books
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PYTHON: PROGRAMMING: A BEGINNER’S GUIDE TO LEARN PYTHON IN 7 DAYS https://t.co/t4wVbsOcJY
Developing Graphics Frameworks with Python and OpenGL https://t.co/VJDGg1wiLq
Python Programming: Your Advanced Guide To Learn Python in 7 Days: ( python guide , learning python , python programming projects , python tricks , python 3 ) https://t.co/8qbl8B4hHY
Python 3 Object-Oriented Programming: Build robust and maintainable software with object-oriented design patterns in Python 3.8, 3rd Edition https://t.co/VzS5AN1VbI
🧵:
PYTHON: PROGRAMMING: A BEGINNER’S GUIDE TO LEARN PYTHON IN 7 DAYS https://t.co/t4wVbsOcJY
![](https://pbs.twimg.com/media/FeZwD2OUcAA7LCU.png)
Developing Graphics Frameworks with Python and OpenGL https://t.co/VJDGg1wiLq
![](https://pbs.twimg.com/media/FeZwGnJVQAIQamu.png)
Python Programming: Your Advanced Guide To Learn Python in 7 Days: ( python guide , learning python , python programming projects , python tricks , python 3 ) https://t.co/8qbl8B4hHY
![](https://pbs.twimg.com/media/FeZwLhdUAAANmSx.png)
Python 3 Object-Oriented Programming: Build robust and maintainable software with object-oriented design patterns in Python 3.8, 3rd Edition https://t.co/VzS5AN1VbI
![](https://pbs.twimg.com/media/FeZwN00VsAA8M9n.png)
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)
![](https://pbs.twimg.com/media/Fp_Pp79agAA36b8.jpg)
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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Those who sold L @ 660 can always come back at 360. Those who sold S last week can be back @ 301
Those who exited at 1500 needed money. They can always come back near 969. Those who exited at 230 also needed money. They can come back near 95.
Those who sold L @ 660 can always come back at 360. Those who sold S last week can be back @ 301
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