## Get started with using TensorFlow to solve for regression problems (Coding TensorFlow)

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You often have to solve for regression problems when training your machine learning models. In this episode of Coding TensorFlow, Developer Advocate Robert Crowe discusses how to build and train a TensorFlow model using Keras, where you are looking for the model to solve for a single numerical result – in other words, regression.. Learn how to get started on regression problems, and watch to see an example involving miles-per-gallon. This requires our model to examine and learn from the data we provide, in order to predict our final number. Subscribe to the TensorFlow YouTube channel for more! → http://bit.ly/TensorFlow1

Get the Colab & follow along here → http://bit.ly/2xV8rVg

UCI dataset repository → http://bit.ly/2k2xH8i

Watch more Coding TensorFlow → https://bit.ly/Coding-TensorFlow

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This was so helpful in many ways in my studies. Thank you very much for the easy to understand explanations

thanks . it's nice to just explain it quick and let people go back in the code and review. I'll check if you have a classification model as well

Thank you, very well explained

Why did you choose the z-score to normalise the datasets ?

I don't understand

I applied this model to my regression problem. But loss comes to be quite high. How to choose a correct model for my regression problem?

I learned more from this single video than my entire last semester, looks easy but it has lots of hidden information. Thank you

i cant give better summary than this in short time. great job! thank you

How can I calculate the R^2 value?

great!!! ihave learnt regression more on here

i want to built ML model for house price prediction using android app what i can do predict the ML model on tensorflow and deploy on android app??

Awesome, I ran the model in Google Colab using tensorflow 2.2.0

Great video. Thank you for sharing!

Splendid lesson, clear lesson. How delicate the way he transmit ideas according python instruction. Love it!

Great video! Thank you!!!

This is Awesomeness!!!!

Hi , Video Series is good. Is Google colab available for R language ?

How to perform regression in deep neural network for image with corresponding numerical value as input? For example, the ROI in the image has specific biomass. And by using DNN with image as input, the network needs to compute for the biomass value. Thanks in advance!

while training the model, it's showing NaN values everywhere. How to resolve this?

why it gives differant output for multiple excecution , even I split data before model bulding () and made shufle= False

What do I have to write in order to get the model to estimate the MPG consumption of a single car in this case of a single row? Like throwing in cylinder, weight, etc. and getting out the MPG of my Bugatti Veyron. 😀

Thanks in advance, Vito.

Can more than one user work on the same file at the same time on colab, if yes How??

I didn't get any thing which one we have to do

great tutorial !

Can i have the dataset !

thanks !

I started with learning Tensorflow, ended looking at 1970-s car videos – how did that happen?

I'm starting with TensorFlow and the video is amazing, Thanks a lot!

Requesting to make videos on NLP using TF and Deep Learning.. thank you so much for sharing such wonderful videos.

When I import data I'm getting shape(19,8) and data is not imported properly, can I import through pandas directly and apply tensorflow?

i have data set wherein the data tells me who is first each class,, if I provide the hole school data the regression model can predict who comes first in each class or in the school as a hole.

I believe that there is a conceptual error on the standardization process. One should estimate the population parameters (mean and st.dev.) from the train set only, and then scale new data (test set, in this case) using the training parameters.

Excellent presentation ! Thank you !

Maybe its a stupid question, but may I know what is a hot-column?

where can i get this notebook?

and.. how does the normalization (standardisation) function even make sense? why the dataframe will match the index of the series automatically?

Very helpful! Thanks so much!

when I want to make predictions with other data, now the output is normalized. How can I get a real value as an output?

Great videos released by a great product like Google Colab. Thank you Google for continuing to push out products and services that are available and widely used by many in different parts of the world

I'm getting following error. Pls help me:

On code:def norm(x):

return (x – train_stats['mean']) / train_stats['std']

normed_train_data = norm(train_data)

normed_test_data = norm(test_data)

Error-I: TypeError: unsupported operand type(s) for -: 'str' and 'float'

Error-II: UFuncTypeError: ufunc 'subtract' did not contain a loop with signature matching types (dtype('<U32'), dtype('<U32')) -> dtype('<U32')

actually this was working very slow as compare to my system

The bit.ly links are already broken Aug 2019. Can you also list the real (unshortened) links? Here is the link to the UCI dataset repository: https://archive.ics.uci.edu/ml/datasets.php but we need you to point to the Colab.

the best , thank you so much

Hi, where do I download the notebook from? Please can someone provide a link?

Hi, I am new to Machine learning . I have a question. what if i have a new data that i want to predict how do i go about doing that? like a new entry that is not in the data set. say i have a new car . maza , 4 clyiner , etc….. what do i do to predict that?

do i need to make a brand new data like

brandnew= {maza , 4cylinger)

model.predict { newdata}?

I appreciate the tutorial, it is great! I have question just to make sure, so the percentage of data used is 64 : 16 : 20 (Training : Validation : Test)? since the validation split is 0.2 or 20% from training data (which is 80% from total data). thanks in advance 🙂

You need a better editor for your videos.

Great! Thanks Robert

Ignoring a warning just like that? That was odd, coming from TF themselves.

don't use tf.keras.optimizers when building the optimizer because it's better to use one version of keras instead of one from tensor flow and then regular keras. Stick with one, otherwise, you will get an error like me! Use keras.optimizers!