Tensorflow and deep learning - without a PhD by Martin Görner
Devoxx6 HN recommenders · 2017–2020
610K views · tutorial · dated · Uploaded 2016-11-09
Martin Gorner's Devoxx talk introduces deep learning with TensorFlow, walking through neural networks for MNIST, how to choose an architecture and how to diagnose networks that fail to train.
Repeatedly called an excellent, practical introduction that covers a lot in two hours and explains issues academic textbooks skip.
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6 comments from Hacker News
For Deep Learning, start with MNIST. The 1998 paper[1] describing LeNet goes into a lot more detail than more recent papers. Also there's an excellent video from Martin Gorner at Google that describes a range of neural networks for MNIST[2]. The source code used in his talk is excellent[3]. [1] [LINK] [2] [VIDEO] [3] [LINK]
Amazing work; it makes using AI and Deep Learning accessible for everyone here really. If you haven't seen it check this out for an intro: [VIDEO] I wish AMD graphics cards were supported fully. I really think AMD should find a way to work with the Tensor Flow team on this...
This is a good introduction focused on tensorflow. [VIDEO] (Tensorflow and deep learning - without a PhD by Martin Görner) The ML/DNN rabbit-hole goes deep. If the video above leaves you wanting more, [LINK] does a good job on drilling into more specifics for the various techniques used. The examples on the tensorflow webpage are also very good.
I watched a great video on Tensorflow (link below). It mostly introduces very basic deep learning concepts, but there are a few key moments in the 2 hour+ video, where he explains what to do if something goes wrong. It's definitely not a "science", but with enough experience in deep learning, you can intuit what's going on inside the black box, and there are best practices on what to try next. For example, he goes through a few examples where a neural net has too many weights, or too little data or improperly connected nodes. All three result in problems, but the problems exhibit themselves in slightly different ways and with expertise you can start identifying them. [VIDEO]
For those interested in a deeper dive to just deep learning, "Tensorflow and deep learning - without a PhD" is really good, and covers a lot of material in a single 2hr talk. [VIDEO]
The video version [1] is also pretty awesome, though its code itself is a bit outdated now. Explains a lot of very practical issues that you might not find in most academic textbooks, but you encounter every day in practice. [1] [VIDEO]