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CS 20SI: Tensorflow for Deep Learning Research (stanford.edu)
396 points by nafizh on March 3, 2017 | hide | past | favorite | 29 comments

Side note, the instructor of that course, Chip Huyen, is nothing short of brilliant.

Born in Vietnam, at 18 she decided not to go to college, and went traveling with an empty pocket around the world and wrote books. I heard about her getting accepted in many tip-top schools and chose to go to Stanford 4-5 years ago at 22-23 year old.

So seems like she is still an undergrad/master student in Stanford or something right now, and she is already teaching a course. Definitely going to go far.

She's honestly one of the most amazing people I've ever met. Go chip!!

That's really cool. I wonder what the admission process looked like for her.

Still had to do the SAT and went through the admission process. She had a lot of publicity in Vietnam at that point, and the rumor was that she had some god-tier percentile on the SAT score, too.

I don't think that was very hard for Stanford to recognize that they have some good stuff.

If anybody interested: i've been learning it and wrote some blog posts about it:


I hope it can be useful for future learners! (you can find the current list of articles at the end of the first one)

That is a very helpful primer. What resources did you use to learn that level of understanding in TensorFlow? Just the codebase? If you have any other articles or references to recommend, please do so. Thanks!

Thanks for this feedback!

I've just spent some time learning TF on my own using the official doc and made a lot of projects on my Githubs.

If you're interested, here is the complete list of my articles so far:

How to handle shapes in TensorFlow: https://blog.metaflow.fr/shapes-and-dynamic-dimensions-in-te...

TensorFlow saving/restoring and mixing multiple models: https://medium.com/@morgangiraud/tensorflow-saving-restoring...

How to freeze a model and serve it with a python API: https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-an...

TensorFlow howto: a universal approximator inside a neural net: https://blog.metaflow.fr/tensorflow-howto-a-universal-approx...

How to optimise your input pipeline with queues and multi-threading: https://blog.metaflow.fr/tensorflow-how-to-optimise-your-inp...

wow... Thanks for these links. I find the offical docs of TF are atrocious and I have a lot of trouble figuring it out.

Also CS231n - http://cs231n.stanford.edu/ - Convolutional Neural Networks for Visual Recognition

FWIW, Udacity has had a Deep Learning with Tensorflow course for a while. (Note that I'm somewhat ambivalent about Udacity - a lot of it is copy-and-paste stuff, though it does help to get you started.)


IMO the Udacity course is very poorly authored and taught. I would not recommend it to someone who has a primary goal of learning TensorFlow unless you have a pretty deep understanding of deep learning to begin with.

They have a nanodegree class that is much better. I have been taking it the past two months and I highly recommend it https://www.udacity.com/course/deep-learning-nanodegree-foun...

What areas have they improved in?

I was really disappointed by multiple Udacity courses; not saying they can't pull it off, but I've been burned enough by Udacity that I wouldn't consider paying for any courses from them at this point.

Yeah, I took most of the "AI for self driving cars" course taught by Sebastian Thrun himself, and was torn.

He's no doubt an extraordinarily competent researcher in his field, but he was clearly a beginner to Python (either that or he didn't care), and the Python code (while bringing the concepts across) was so poor as to be distracting.

I have watched a lot of Siraj's YouTube videos. He is a very entertaining speaker, but never felt like I actually learned a ton. Is the nanodegree on Udacity better?

it's very poorly organized. I'd say you get maximal utility out of it if you're like me and for some things (I don't always do this) have to pay for the privilege of being pressured to do something to completion, and are reasonably confident you can figure things out yourself (good maths background, minimally "modestly capable programmer", maybe have tinkered with ML).

Hopefully future iterations will be better.

I have taken a 'certification' and a 'class' with Udacity and I wouldn't recommend it to anyone. The courses are shallow and do not challenge the student and the certifications hold no significance.

I prefer EduX courses as they are more like 'classes' and I feel are slightly more respectable to put on a resume.

Are the videos also somewhere or only slides and notes are shared?

Not at the moment, but there are some walkthroughs on YouTube which aren't bad at all: https://www.youtube.com/watch?v=g-EvyKpZjmQ

Came here to ask the same question.

From the FAQ: Will lectures be recorded? Because this is a student initiated course, the lectures won't be recorded.

I hope that changes in the future! Looking for a really good course on TensorFlow. Udacity's Self-Driving Car nanodegree is also very hands-on in terms of learning TensorFlow. You build your own mini version.

Are there any other university level courses for Tensorflow available?

MIT had a short, week-long 'Intro to Deep Learning' course that had some labs in Tensorflow.


CS224d - Deep Learning for Natural Language Processing, is using Tensorflow for assignments and has an introductory lecture on Tensorflow.


Just saw a youtube channel, which present the slides from this course as they available. https://www.youtube.com/channel/UCMq6IdbXar_KtYixMS_wHcQ . HTH

Are the videos available for this course?

Thanks for sharing, I've been looking for a resource like this for a while!

Thanks a lot! I've been looking for something like this.

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