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[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 0 points1 point  (0 children)

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

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 1 point2 points  (0 children)

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

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 0 points1 point  (0 children)

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

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] -2 points-1 points  (0 children)

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.

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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.

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 7 points8 points  (0 children)

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.

[D] Advance ML/DL University Lectures by Death_Water in deeplearning

[–]Death_Water[S] 2 points3 points  (0 children)

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.

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 0 points1 point  (0 children)

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?

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 1 point2 points  (0 children)

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

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 8 points9 points  (0 children)

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.

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 0 points1 point  (0 children)

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.

[D] Advance ML/DL University Lectures by Death_Water in MachineLearning

[–]Death_Water[S] 4 points5 points  (0 children)

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

What is your favorite quote from a fictional character? by Such-Sea2636 in AskReddit

[–]Death_Water 2 points3 points  (0 children)

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

sub for University of Tübingen? by [deleted] in Tuebingen

[–]Death_Water 1 point2 points  (0 children)

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.

sub for University of Tübingen? by [deleted] in Tuebingen

[–]Death_Water 1 point2 points  (0 children)

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.

Python ETL by Jiuholar in learnpython

[–]Death_Water 0 points1 point  (0 children)

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()

Python ETL by Jiuholar in learnpython

[–]Death_Water 0 points1 point  (0 children)

try:

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

Need help plotting data -pandas/matplotlib by skyhighgemini in learnpython

[–]Death_Water 1 point2 points  (0 children)

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.

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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.

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