Plain and Simple Estimators
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- čas přidán 6. 09. 2017
- In this episode of AI Adventures, it's time to write some TensorFlow code! We'll build a linear model to recognize different kinds of flowers using a canned estimator.
Learn more through our hands-on labs → goo.gle/2B2f0Kp
Associated Medium post - Plain and Simple Estimators: goo.gl/L2YUbk
Jupyter notebook: jupyter.org/
Code from this episode: goo.gl/ChcaM9
TensorFlow Estimators: goo.gl/r1tZUW
Dataset: goo.gl/UtccyH
Watch more episodes of AI Adventures: goo.gl/UC5usG
Subscribe to get all the episodes as they come out: goo.gl/S0AS51
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this video was created with tensorflow V 1.3.0, todays version is 2.9.1. contrib is removed. please consider updating the video.
Yufeng, your job is really good in having these videos to teach ML for people. My only advice is to number the videos, so that people like us in Africa will find it easy to follow the videos to learn ML/AI
Wondeful. Looking forward to using Tensor flow soon.
Awesome series!!! Thanks a lot.
nice series.. looking forward to watch the new video 👍🏻
Awesome Playlist!
very nice video
This is quite a nice intro to ML. I like the idea.
Excellent. Brief. Clear. Thoughtfully highlighted and focused presentation. Mr. Guo easily understood.
Great job,man!
AWESOME! thanks for this video series.
Very good job !!! Its first time that understand the ML Please go on
We did the same example in Multivariate Analysis. Really concise and simple to follow video. But how would we do it using images?
Fantastic!
great video.
Hi Yufeng, great video. However, when I tried to code it by hand, or even use the notebook you supplied, I realised that my Tensorflow is version 1.1 instead of 1.3 that you have. And this is in January 2018.
Wow... superb
So basically we need to convert the Species column to integers? When watching this tutorial, somehow I feel like that the csv loading function will convert them into factors by default. If a conversion is required, why isn't the processed data provided for downloading? Thanks
It seems that the sessions run through a Estimator are not quite the same as a normal session? For example, `.eval.()` operation and the `tf.nn.sampled_softmax_loss` function do not work, when they do for a normal session. Would love to hear some details on this phenomenon.
How do you create a prediction based on this model ?