thank you! got what i was looking for

Watching this again reveals even more ideas! Many thanks for this Dan. Also do you have similar ones using Datashader and hvplot using pandas / polars /dask for plotting line,bar charts etc?

Thanks for the video! One question, what to do when I have z as pd.Series and not as a matrix? Not sure what would be the right way to convert it to matrix. I can use reshape, but I'm not sure it will shape the matrix as required.

Hi Dan, got the following error in cell [7] when trying to run your script in Colab: --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-7-f76c603a8d2f> in <cell line: 1>() ----> 1 feature_matrix_customers, features_defs = ft.dfs(entities=entities, 2 relationships=relationships, 3 target_entity="customers") 1 frames /usr/local/lib/python3.10/dist-packages/featuretools/utils/entry_point.py in function_wrapper(*args, **kwargs) 30 # call function 31 start = time.time() ---> 32 return_value = func(*args, **kwargs) 33 runtime = time.time() - start 34 except Exception as e: TypeError: dfs() got an unexpected keyword argument 'entities'

awesome intro

Hi Dan, this was a great introductory video. I am learning Dask and this was very helpful.

Thanks for the videos. I have a question about multivariate data. I have three independent variables and would like to see their occurrences by coloring the data based on their probability densities (plot type can be contour, surf etc.) Which function should I use? Could you please help me with this?

Hey Dan, can you explain how to change the color scale in green - yellow - red?

Great video, worth trying the notebook after reading the paper

Great

I've added dask delayed to some functions. When I visualize it, there are several parallel works planned, but my cpu does not seems to be affected (its only using a small percentage of it)

Thanks man. It's really hard to find some information about dask

4:12 ,visualize() , where can in get documetation? I tried to use for sorted() did not work

Hi 😀 Dan. Thank you for this video. Do you an example which uses the apply() function? I want to create a new column based on a data transformation. Thank you!

How to schedule a dask task? for example how to put a script to run every day at 10:00 with dask for example

Saved my ass, man. Thanks!

Many thanks for this excellent video! It is really clear and helpful! I just have one question, I tried to run the notebook, and ran pretty well after some minor updates. Just the last line I was not able to make it run: # never run out-of-memory while training "model.fit_generator(generator=dask_data_generator(df_train), steps_per_epoch=100)" Gives me an error message: InvalidArgumentError: Graph execution error: TypeError: `generator` yielded an element of shape (26119,) where an element of shape (None, None) was expected. Traceback (most recent call last): TypeError: `generator` yielded an element of shape (26119,) where an element of shape (None, None) was expected. [[{{node PyFunc}}]] [[IteratorGetNext]] [Op:__inference_train_function_506] Any recommendation on how I should modify it to make it run? Thanks AG

very good ! thank you ! :)

fantastic video must say,,keep going sir...ur really really great...teaching such a complex thing in such short video ythat too so clear...thanks a lottt again

Great job on this video, Dan! Datashader is pretty sweet!

👏👏

Great video! Thank you man 👍

Thank you. You should come back and bring more content.

Nice demo. Thank you for sharing. Keep up the good work.

Many thanks your very sophisticated steps by steps instructions! I would like to ask you how we can reach these kind of data set like more than a millition being able to use datashader?

Great Video an Explanation! Thank you very much for it! IT is really helpful! I tried to run the notebook, and it ran pretty well after some minor updates. I just had problems to run the latest part. "never run out-of-memory while training", seems the generator or steps per epoch part is giving some prblem I cant fogure hout how to solve. Any possible suggestion on how to fix the code? Thanks! InvalidArgumentError: Graph execution error: TypeError: `generator` yielded an element of shape (26119,) where an element of shape (None, None) was expected. Traceback (most recent call last): TypeError: `generator` yielded an element of shape (26119,) where an element of shape (None, None) was expected.

Man, great video, thank you!

Thanks Man!

Great explanations for beginners!! Thanks for this...

Hi, trying to do your excerise code , but an error appears : TypeError: dfs() got an unexpected keyword argument 'entities'.

Very nice video!!!! Thank you so much!!! Pls make more about this hands-on video! You explain them very clear and helpful!!!!

amazing. Very interesting theme.

wonder what is difference between encoding and mapping. For example if STATE_CD goes from 1 to 50 say, now its numeric - can it be used in AI learning without resorting to one hot encoding?

Chapeau, well explained and healthy usage of memes!

Great intro. Thank you

Great video! Thank you for sharing.But I think your code would have some incorrect codes in machine learning with dask part. There is no X in the code (model.add(..., input_dim=X.shape[1], ... ) and when I training model.fit_generator, the tensor flow saids model.fit_generator is deprecated.. and finally displayed error - AttributeError: 'tuple' object has no attribute 'rank'

Great video with good examples. Loved the MiniNet part. Thank you.

Hey thanks for the awesome video and the explanation. I have a use case. I am trying to build a Deep learning tensorflow model for time series forecasting. For this I need to use multinode cluster for parallelization across all nodes. I have a single function which can take data for any 1 store and predict for that store. Likewise I need to do predictions for 2 lakh outlets. How can I use dask to parallelize this huge task across all nodes of my cluster? Can you please guide me. Thanks in advance.

I really appreciate the batch-on-the-fly example with keras.

Great content! One question, isn't it strange to use function in this form: function(do_something)(on_variable) instead of function(do_something(on_variable)) ?

Really great Dask introduction and the explanation is so easy to understand. That was useful. Thank you!

Many thanks. Now I understand why the file was not read

Nice

Good work sir, your video has helped me to get started with Dask. Thank you very much.

This is really greAT

Great. I would also like to see more on DASK and Deep Learning. How exactly would this generator be used in pytorch? Instead of the DataLoader. Thanks for the video(s)

יש משהו דומה על למידת מכונה?

Excellent!

Thank you so much for your explanation, I learned more in this one video than reading multiple articles where my mind felt bogged and bored each time I read a line but this is so digestible and easy to understand

Dan, I just found your videos. They’re great! Will you be making any more?