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

[jamesthompson]

This is a very elegant piece of code. It's worth while to pause and study it.

[MilindParvatia]

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

[antimatterwt]

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

[oliverli]

great!! there're too much classification vids, but no regression vids. nice to see this

[asuagar]

In 3:14, there is a other way to do the One Hot Encoding using the pandas get_dummies function:

dataset.Origin = dataset.Origin.map({1: 'USA', 2: 'Europe', 3: 'Japan'})
ohe = pd.get_dummies(df.Origin)
dataset = pd.concat([dataset, ohe],

By the way, thanks for the videos!

[genoalam]

Normalization does not lead to features between 0-1 rather it puts the features around 0 with std deviation of 1. It gives all the features equal variance so that no weight gets too powerful (around 6 minute mark)

[aragorngamer]

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

[CardioCareWellness]

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

[akilaj]

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

[anuragsharma]

A minor correction: What we did here during preprocessing is Standardization (in terms of Z score) not Normalization (Min-max scaling). great video though!

[anthonyfernandezgonzalez]

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

[notarealhandle]

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

[haseebtubing]

This is amazing. Thank you for sharing!

[tmusic]

At 9:30 - overfitting; At smaller sample sizes e.g. via sampling of the total set, least square coefficients could be used as reference estimates (b=inv(X'X)X'y). At 10:40; why not plot the prediction errors to get outliers? And at 10:54, why not explore the residuals? Deviations from the normal distribution usually generates interesting information about the sample. Information that can be used for model improvement.

[aomo]

HI, Thank you for this video;
Please why you have chosen 64 for Dense.
Thank you

[SanataniAryavrat]

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

[pieterherings]

Great video!
Two questions;
- Where do you define that you're predicting the MPG and not something else?
- My PrintDot function is not recognized, I get a NameError everytime I try to run the code (im sure there are no typos). Anyone knows what the problem could be?

[DimasAnggaFM]

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 :)

[vitotonello]

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. :D

Thanks in advance, Vito.

[tripzero]

Perfect! This tutorial helped me upgrade my old tensorflow code to use keras. Good tip also about normalizing. That said, for some reason the early_stop callback stops training after about 10 epochs which is way too early!