D3DShot
Extremely fast and robust screen capture on Windows with the Desktop Duplication API (by SerpentAI)
d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. (by d2l-ai)
D3DShot | d2l-en | |
---|---|---|
1 | 6 | |
256 | 21,704 | |
- | 3.5% | |
0.0 | 8.5 | |
almost 2 years ago | 9 days ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
D3DShot
Posts with mentions or reviews of D3DShot.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Part 12a: Real to Reel
By using the Python package d3dshot, we can grab a screenshot of our RealFlight environment (we'll take just the part showing the downward-facing camera feed), and then send this image data (encoded using OpenCV) over UDP. On another computer we can have a script running with a UDP socket open and waiting to receive these messages. This script is representing the third-party peripheral, which in real life would be capable of obtaining the video footage on its own. Nonetheless, the peripheral now has its data to analyze. The important thing to note here is that this peripheral is self-contained. It is not part of the autopilot (it's written in Python, for one thing), and thus its hardware and software can be developed without any integration concerns, provided that it conforms to the autopilot's API. So while this "peripheral" currently exists on the same computer that is running the autopilot, this is by no means a constraint, and it will soon be moved to a separate piece of hardware. So what, you may ask, it the point of this peripheral? Well, that will just have to wait until next time. But I think it's pretty cool, so hopefully you'll come back to check it out. You're very patient, dear reader. That's what I appreciates about you. -Greg
d2l-en
Posts with mentions or reviews of d2l-en.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-10.
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
-
How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
-
The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
-
I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.