Intro to Feature Engineering with TensorFlow - Machine Learning Recipes #9
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- Опубликовано: 7 сен 2024
- Hey everyone! Here’s an intro to techniques you can use to represent your features - including Bucketing, Crossing, Hashing, and Embedding - and utilities TensorFlow provides to help. Also included is a walkthrough of using TensorFlow Estimators to classify structured data.
Links from the video:
Code - goo.gl/K9dVqv
Facets: goo.gl/Dfpb7W
TensorFlow Embedding Projector: goo.gl/2SxrYK
You can find Josh on Twitter: / random_forests
See Josh as a guest speaker in Week 2 of the openSAP course: goo.gl/UGGcX7
Thanks, and have fun!
Check out more Machine Learning Recipes here: goo.gl/KewA03
Subscribe to the Google Developers channel: goo.gl/mQyv5L
It is very short but every second matters. Seriously well designed well compressed contents. I appreciate Josh Gordon and Google!
I think this should be transformed into a complete series in machine learning engineering. Please, try to develop such a curriculum as it would tremendously helpful not only for developers but also for researchers!
Please keep this series going.
Great job at explaining concepts in plain terms. John Gordon is a master at making hard concepts easy to even a nine year old child. Thank you!
Thanks for creating this series Josh Gordon you have increased my understanding of ML/AI greatly and I hope you keep this series going
For those who had problems with the link, there is: github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
He's finally back!
thanks Josh Gordon..learnt a lot from your videos...very much informative... hope you wil continue the series... waiting for next one
Nicee I just watched all the videos until this episode.
And i must say it is the only ML course i can realy understans!!
great job done by google, simplifying this by such great examples and awesome videos. Keep sending more please. Thank You!
the code link is leading to a 404 page
github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
Excellent content
It will be great if you could provide links to few reference materials for reading (or examples)
For people coming here post 2018, here's the link to the jupyter notebook - github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
You are back, thanks for the tutorials
His plan is to complete the course by year 3017
Thanks for such awesome videos :) The code link is not valid any more whats new link ?
Thank you. Looking forward to more on feature engineering.
For the education example you say, "the best way to represent this, is just to use the raw value", but if there was a roughly linear relationship between increasing education category and your earning, could you not then re-code it as an ordinal numeric variable in order to save a few degrees of freedom / learn only a single parameter?
Thanks for the valuable information, Please keep this videos going on.
He's back
What's difference between hashing and embedding
keeping coming!
awesome video series!
Sweet. Thanks again
Fantastic video
No code with link?
New code link: github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb. Thanks @Catherine Rakama for sharing this.
Another great video! Is there an embedding feature in keras.perprocessing?
Great visualizations!
Very informative!
ML made easy , Please have series related to internals of ML algorithms .
It's amazing buddy.... 👌
Please upload more videos and include reinforcement learning
Very nice tutorial but the link to the code is not opening, please kindly give correct link to the code, please, thanks
why the code is not available?/
Thank You
Awesome
Great....thanks for these videos and good nformation
the code isn't in the link
Provided link is not working, here is :
Codes in video :
github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
Thats fantastic....
New link to the code is at github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb. Or just search his github repo under archive folder
go on!
Are there any new videos going to be uploaded in this series ?
This is just like interaction effects
Thank you ☺
Link of code that is missing github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
link for the code has change to this: github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
After installing tensorflow...when I tried to import tensorflow in python, it showed module not found...can you please help me to solve the problem
Make sure your Jupyter notebook works then google "tensorflow jpynb" and make a choice to start
the link to the code of this episode does not work!!
The code is no longer available on GitHub, or it has been moved
Just search his repo and you will find it inside archives - github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
Link "Code - goo.gl/K9dVqv" doesn't work
Is this series over
Thanks for your videos :). The code link was changed. this is currently github.com/tensorflow/workshops/blob/master/extras/archive/07_structured_data.ipynb
Here is the code:
github.com/tensorflow/workshops/blob/master/extras/archive/07_structured_data.ipynb
Found code here... github.com/random-forests/tensorflow-workshop/blob/master/archive/examples/07_structured_data.ipynb
that red crossing is incredibly distracting
Recipe #10 ........
Is there a good book about tensorflow?
hands-on machine learning. www.amazon.com/Hands-Machine-Learning Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_1?s=books&ie=UTF8&qid=1510436181&sr=1-1&keywords=hands+on+machine+learning+with+scikit+learn+and+tensorflow
Please keep it going _ /\ _
google: 07_structured_data.ipynb
What took you so long ?? Are you on deadline or what?
受益匪浅
?????
Thank You
Thank you