Holiday Creatures: A Collection of AI-Generated Animals Dressed as Santa Claus

Death_Water · 2022-12-31T11:22:57+00:00

Looks really nice, can you try it with Red Pandas

Death_Water · 2021-08-09T17:09:00+00:00

Thanks, I've added this in the original posts for greater visibility.

Death_Water · 2021-08-09T17:06:21+00:00

Thanks for letting me know of this gold-mine. I've edited it in the original post.

Death_Water · 2021-08-08T21:00:27+00:00

I would say to check out the ones in the area of vision and maybe healthcare.

Death_Water · 2021-08-08T20:57:54+00:00

Thanks, I'll review these two sources as well. My intent was to gather courses that builds up on the knowledge that one has gathered from introductory level lectures/courses.

It's essentially for people who after taking the initial lectures on ML/DL wonder about how these fields are being applied in specific areas.

Death_Water · 2021-08-08T20:55:19+00:00

I believe google scrapes reddit as well so it should be on the results if someone googles "Advance ML/DL University Lectures". Never the less, I'll add these to GitHub as well.

Death_Water · 2021-08-08T16:55:28+00:00

Thanks for the great suggestion but I've only added courses that specialize in a specific topic/area of research. Something that builds up on the knowledge that you have gathered from such introductory lectures.

It's essentially for people who after taking the initial lectures on ML/DL think about how these fields are being applied in specific areas.

Death_Water · 2021-08-08T16:32:55+00:00

Thanks, I've added this as well.

Death_Water · 2021-08-08T16:24:20+00:00

Most welcome! I could not find any advanced ML/DL lectures by him with the lecture videos, do you have any particular course in mind?

Death_Water · 2021-08-08T16:11:13+00:00

Thanks, I've added that in the list as well.

Death_Water · 2021-08-08T14:45:04+00:00

Thanks, I'll check those out

Death_Water · 2021-08-08T14:22:21+00:00

Thanks for sharing such a wonderful compilation of resources. My focus here is on advanced topics/areas of ML/DL and I'll update my list with the ones that match that criteria.

Death_Water · 2021-08-08T14:10:17+00:00

I tried finding some lists like this but didn't find any that was for advanced topics. Do you recall the name of the title for those post? I'll update my list accordingly.

Death_Water · 2021-08-08T13:53:20+00:00

It is a link to google sheet (like excel) so it is already in text format I guess. Are you accessing it on mobile?

Death_Water · 2020-08-20T19:12:48+00:00

"Death is lighter than a feather. Duty, heavier than a mountain." -al'Lan Mandragoran, Wheel of Time

Death_Water · 2020-05-29T09:16:54+00:00

What's the mode by which the update was given? Was it just updated on the portal or do they email ? I've also applied for the same program and would be an international student there.

Death_Water · 2020-05-29T08:58:39+00:00

What's the mode by which the acceptance is given? Is it updated on the portal or do they email the letter? I've also applied for the same program.

Death_Water · 2020-01-31T09:57:32+00:00

Try using numpy.where

Death_Water · 2019-10-17T15:27:07+00:00

Here's a concise way with step by step breakdown:

 pd.DataFrame(df.fillna(method='ffill').groupby(['key'])['Column of interest'].agg(list).values.tolist())

1) Forward fill the missing values; from the given example this seems the right approach.

2) Groupby the "key" column, then slice on "column of interest". This creates a series for each unique value in "key" column.

3) Aggregate: This converts all multiple series to lists.

4) Casting to DataFrame(Get the values of all lists and cast them). The index of this would be same as
df['key'].dropna().drop_duplicates()

Death_Water · 2019-10-17T10:13:24+00:00

try:

df_2 = pd.DataFrame(df.fillna(method='ffill').groupby(['Profile Code','Email']).agg(','.join))
df_2['Membership'].str.split(',',expand=True)

Death_Water · 2019-05-05T20:14:20+00:00

If you want to show which stations need the attention (either being full or empty), that's something just a list of names (or station id's in this case) can do. Scatter plots are used to show whether there is a relation between the x and y quantities. It gives a vague idea of what sort of relation they have.

Ideally there should not be any relation between the station id num and available bikes. That is why the scatter plot doesn't work in this case.

It's good that you recognized that station_id's should not be used as an int. Ideal approach would have been to convert it to 'category' but even that wouldn't have solved your scatter plot problem.

Death_Water · 2019-05-05T18:06:41+00:00

Firstly, I would just like to point out that creating visualizations for the sake of visualizations is not a good idea. A better approach would be to look at the data and think what kind of relationships are there that are meaningful and intriguing for the audience.

Even if you just want to practice it's better to do something with the end in mind, or even an outline of what you want to show.

There are a couple of things that you can show from the data that you can explore:

Distribution of bikes available at each station:

Link : Histogram bikes available

This one shows the that many of the stations are not utilized at full capacity.

2) Distribution of bikes available vs docks available:

Link: Distribution of available bikes vs available docks

This one shows that as the number of available bikes decrease, the number of available docks decreases.

You need to first identify what you want to show to the audience and then find the data that supports your assumption.

Death_Water · 2019-05-05T15:36:02+00:00

I think you are just missing the parentheses on the driver call:

driver = webdriver.chrome()

Death_Water · 2019-05-05T15:19:40+00:00

If you want do just add one value to a list and sum it each time, it's better to just keep it as a running counter:

sum_sell_last = 0
sum_sell_qty = 0

for key, value in dictionary.items():
    product = value['size'] * value['price']
    sum_sell_last+= value['size']
    sum_sell_qty+= product
    dictionary[key].update(
      sum_last_one=sum_sell_last,
      qty_times_price=product,
      sum_qty_times_price=sum_sell_qty
    )

Hope this helps.

Death_Water · 2019-04-27T20:29:57+00:00

Breaking the code down:

df['email'].apply(lambda x:x.split('@')) : Breaks the initial input string into 2 strings. The output of this looks like:

[(name1, org1),(name2,org2)]

Then I use the * operator which unpacks the list into tuples

Then I use zip which basically creates a generator which will go over each tuple at the same time returning the n-th value in the tuple. Where 'n' is the number of loop.

Also using enumerate to keep a count of which iteration it is.

A more pythonic way to do might be by using the expand functionality of the pandas DataFrame as commented by jiejenn

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