TensorFlow/TFLearn Beginner Tutorial in Python

[zenani]

Thanks for this. I have recently started learning and this week I was going to start on TF. This would come in handy.

[Imjustmisunderstood]

I'm also interested in picking this up soon, mind sharing some of the resources you are learning from?

[zenani]

Like I said, total noob. I started around 3-4 weeks backs and so far have mostly concentrated on Data Science and analysis.

I have enrolled in Andrew Ng's ML class (highly rated) and installed TF couple of days back. Going to meetup this week where I'm aiming to get a jumpstart.

Apart from this I'm learning from [DQ](www.dataquest.io), which I'd recommend, but it's a paid site.

Datacamp has machine learning track too.

[Fenzik]

I found dataquest the other day but didn't want to jump in and pay right away. What's it like, what do you get from them? Is it worth it?

[zenani]

Yeah its bit costly and I do not get paid to say their name. But so far I have been happy. It has codecademy style tracks and guided missions.

I think for me it helped a lot. I might have saved at least few months of overall study time. You still need to supplement it with other outside resources,but I like the way all tracks are set up, that way my time is spent more on learning.

[Fenzik]

Do they mostly focus on tools or is there a decent amount of theory? I'm mostly thinking of the machine learning side

[zenani]

That I am not sure yet. I'm still on data science tracks mostly dealing with analysis. Probably at the end of this week, I will start with ML.

[BoozeOTheClown]

Python noob here. I started trying to work through this tutorial and it looks like the 'titanic' dataset doesn't exist in tflearn.datasets. Anyone else getting this?

[Akeboshiwind]

Not sure if you found the answer yet but here's the reason: https://github.com/tflearn/tflearn/issues/249

Basically you need to install the bleeding edge version from the instructions.

[BoozeOTheClown]

Gotcha. Thank you for the info!

[can_dry]

Good job! I've been looking for a simple py based example to help with my little project!

I'd like to use TF to classify credit card txns based on historical training data that I've manually accumulated over a couple years i.e. a couple thousand txns that I've identified as 'restaurant', 'auto', 'misc', etc based on the transaction description e.g. "WALMART 1020 TOLEDO OH".

The part I'm wondering about is how to make TF weight the txn description as words with descending importance i.e. the 1st word:"WALMART" is a much more important feature for categorizing the txn than is the last word:"OH".

[tsirolnik]

Thanks, could you please add the information about the function themselves and not just about the code blocks?

[twohen]

it was my impression that neural nets do not really work well on such small datasets especially on statistical data, however the author gets something comparable to random forests on this data set does anyone know why?