31–40 of 47 posts
So, in other words, learn to slap layers'n'shit together. I thought that's what everyone is already doing in deep learning.
[1] https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A
PS: More generally, is there a guide that explains how to robustly encode real numbers as output of neurons? I've tried to search for it, but couldn't find it.
I watched a version of this course a few weeks ago and it has cleared up a lot of things for me. Martin doesn't waste time on basic concepts and covers a lot of ground in 3 hours. It's probably the tutorial I learned the most from so far.
Perhaps somebody here can help me with a sideproject that I'm working on. I'm trying to figure out the topology of a neural network that is capable of detecting the location and orientation of a given object. Say, a wrench. I don't want to use heatmaps (e.g. [1]) because they give just the location of the object and not the orientation. So the problem is basically how to choose the output quantities and how to encode…
Perhaps somebody here can help me with a sideproject that I'm working on. I'm trying to figure out the topology of a neural network that is capable of detecting the location and orientation of a given object. Say, a wrench. I don't want to use heatmaps (e.g. [1]) because they give just the location of the object and not the orientation. So the problem is basically how to choose the output quantities and how to encode…
What I'd like is a Learn TensorFlow and deep learning with a mathematics/physics background.
Fancy math is useful for explaining why it works.
But this sort of content is good for explaining to engineers -how- it works. Which is ultimately how I need to understand things before the why is interesting to me.
Of course, 9 lines is a little dense, even with numpy. In practice, I got more understanding out of the slightly longer version that clocks in at 74 lines including comments and empty lines. This is an enormously simple neural network: a single layer with just 3 neurons. My son described its intelligence as being less than a cockroach after it'd been stepped on.
It works though. It's able to accurately guess the correct response for the trivial pattern it's given. You can follow the logic through so you understand each simple step in the process. In a follow up blog post, there's a slightly smarter neural network with a second layer and a mighty 9 neurons.
These examples are very approachable. It's about as simple a neural network as you can get. If you're new to machine learning, understand how it works helps illuminate the more sophisticated networks described in Martin Görner's presentation.