spotty | Training deep learning models on AWS and GCP instances | GCP library

I have a pop-up modal which works overall, however the one annoyance is it has a hardcoded max-height which I'd like to eliminate.

Option #1: Initially I explored using height: auto on the modal, which does keep the modal height to the natural height of the contents. However this effects the collapsing of the modal when you scale the browser viewport to a short height. The modal overflows out of the viewport, instead of only the green image area overflowing.

Option #2: I'm aware of the possibility of max-content (for height... or even max-height ?) but I haven't been able to get it to work anywhere, and anyhow it has spotty browser support.

Option #3 (current): Setting the modal to height: 100% and max-height: 500px is good enough, however obviously the content needs to be shorter than that.

Overall, requirements are:

A - In small screens, the modal should collapse with the green image area overflowing, thereby maintaining modal title and buttons in view.

B - In large screens, the modal height should only be as big as the contents.

C - Whatever happens, the modal should never visibly go past the global padding (2em).

See #modal in CSS below:

Demo and code here (Codepen)

I have a df with time series data of non-regular and spotty (yearly) data. It contains a column for the year, the country, and two values, like this:

I found this post which asks a similar question. However, the answers were not what I expected to find so I'm going to try asking it a little differently.

Let's assume a function searching_for_connection will run indefinitely in a while True loop. It that function, we'll loop and preform a check to see if a new connection has been made with /dev/ttyAMA0. If that connection exists, exit the loop, finish searching_for_connection, and begin some other processes. Is this possible to do and how would I go about doing that?

My current approach is sending a carriage return and checking for a response. My problem is that this method has been pretty spotty and hasn't yielded consistent results for me. Sometimes this method works and sometimes it will just stop working

I am trying to access the google reseller api using the nodejs library, which has very shi..., I mean spotty documentation. I tried following the example here, but I fail at step 3 with this error:

Hi Sarem

Background

I have an application that detects when somebody says 'Hi Sarem' as a kind of electronic lock. I wanted to do something like 'Hi Siri' but since that is taken I went for something a bit different, like 'Hi Sarem'.

Implementation

The code samples audio from the mic, fits an FFT and then checks for three consecutive frequencies, so you could trigger it if you e.g. whistle or play the correct three notes on a piano as well. Those frequencies need to be triggered within a certain time from one another and are configurable using the sliders. The code contains the parameters you need to set timings and tolerances and so on. The three sliders represent the three 'notes' in 'Hi-Sa-rem'.

UI

The image here gives an idea of the UI. As the relevant frequencies are detected the bullets turn red and once the whole sequence is detected the big one turns red. The slider at the top acts as a monitor that continuously monitors the frequency 'heard' so you can use that to calibrate the notes.

Problem

I have a few problems with this. Accuracy is a big one but not the primary one. (I think if I had a scarier mama this might have been more accurate and also done by lunch but that is another story ...)

So here goes - the primary problem.

This works decently on a device, but on a simulator I get the following in the log

Is there a guide anywhere for serializing and restoring Estimator models in TF2? The documentation is very spotty, and much of it not updated to TF2. I've yet to see a clear ands complete example anywhere of an Estimator being saved, loaded from disk and used to predict from new inputs.

TBH, I'm a bit baffled by how complicated this appears to be. Estimators are billed as simple, relatively high-level ways of fitting standard models, yet the process for using them in production seems very arcane. For example, when I load a model from disk via tf.saved_model.load(export_path) I get an AutoTrackable object:

Its not clear why I don't get my Estimator back. It looks like there used to be a useful-sounding function tf.contrib.predictor.from_saved_model, but since contrib is gone, it does not appear to be in play anymore (except, it appears, in TFLite).

Any pointers would be very helpful. As you can see, I'm a bit lost.

I am currently working with a panel data of financial information on pandas, and I am trying to generate a column of cumulative abnormal returns for 3-year on a rolling basis. Unfortunately my data is a bit spotty and therefore for the same company I might have a gap in the years. This means that I can not simply apply .rolling(3).sum() because we risk of adding years that do not belong with one another. Just to give you an idea, here is an example of my df:

I am currently working with a panel data of financial information on pandas, therefore working with different companies across different years. I am trying to generate a column of the $ invested shifted by 2 time periods. Hence, reporting the value of time t also at t+2.

Normally, to lag a variable, I would use df.groupby('tic')['investments'].shift(2) , however unfortunately my data is a bit spotty and therefore for the same company I might have a gap in the years. Just to give you an idea, here is an example of my df:

I am working with a large panel data of financial info, however the values are a bit spotty. I am trying to calculate the return between each year of each stock in my panel data. However, because of missing values sometimes firms have year gaps, making the: df['stock_ret'] = df.groupby(['tic'])['stock_price'].pct_change() impossible to practice as it would be wrong. The df looks something like this (just giving an example):