20q | 20 questions in python/pygame | Artificial Intelligence library

1. geom_repel_text(..., size = 15)

2. Write a "labeller" function that uses abs.

   Note: perhaps you meant to negate all instead of just absolute value? In that case, you can use `-` instead of abs, or you can use scale_y_reverse.

3. scale_*_continuous(name="quux", ...)

As @andrew_reece suggested, facet_* will help here.

Since we don't have any notion of "Responders" in the data, I'll blindly duplicate the data using tidyr::crossing.

Also, just for the sake of demonstration, I removed theme_classic in order to highlight the panes. (There is no problem with using it, I just wanted to make clear the difference.)

Check out curl Documentation here.

Curl does not work like a web-browser. Web browsers send multiple requests like Cookies,pragma etc. Also, URL syntax is based on Protocol (HTTP, HTTPS, IMAP etc) so it does not return what you expect as curl can not perform nested tasks.

You can also see the documentation of Curl on you distro. man curl

This is the header for Stackoverflow sent from my browser/

angular.module gets name parameter as string:

Q-learning is an instance of the Bellman equation applied to a state-action value function. It is "model-free" in the sense that you don't need a transition function that determines, for a given decision, which state is next.

However, there are several formulations of Q-Learning which differ in the information that is known. In particular, when you know the transition function, you can and should use it in your Bellman equation. This results in the equation you cited.

On the other hand, if you don't know the transition function, Q-learning works as well, but you have to sample the impact of the transition function through simulations.

Well, I have made work - by removing BatchNormalization layers. Now I am completely mystified... so does batch normalization work differently in Keras and Tensorflow? Or is the missing clue this mysterious "training=True" parameter in TF (not present in Keras)?

PS. While digging into the issue, I also found this very useful article describing how to create advanced Keras models with several inputs like masks (like in the original TF code!):

https://becominghuman.ai/lets-build-an-atari-ai-part-1-dqn-df57e8ff3b26

Did you try getting the headers by loading the target URL directly?

I sent a GET request to https://cs.cruisebase.com/cs/forms/hotdeals.aspx?skin=605&nc=y with the following headers, and I was able to get the complete response.

This is how I would do it: I would save the glyphs as symbols. Please observe that the symbols have a viewBox attribute. This is allowing me to resize the glyphs as needed.

Also I've transformed the paths transform="scale(1,-1) translate(0,-1700)".

Thank you very much to @MoshFeu for this comment:

It should be rotated like this:i.stack.imgur.com/oC6qn.png – Mosh Feu

I'm saving the symbols in an svg element with width="0" height="0" and position:absolute;. This way the svg element with the definitions is not interfering in the layout. Now you can use the glyphs with a element giving them the size and the position you need:

Could be possible that they had logged your previous scraping activities? That Scrapy is not responsible? At all?