←Back to Beyond Market Intelligence
•1 min read•from Machine Learning

I wanted to watch a neural network learn [P]

Our take

Curious about the inner workings of neural networks? One developer built an interactive demo allowing you to directly observe a network's learning process. Explore how adjusting the architecture—specifically, the number of layers and neurons—impacts the function it approximates. A fully-connected network with ReLU activations produces piecewise linear functions, with the number of segments directly tied to layer width. For example, a single layer with three neurons can create up to four segments.
I wanted to watch a neural network learn [P]
I wanted to watch a neural network learn [P]

I wanted to really see how a neural network learns different functions, so built an interactive demo. You can change the architecture of the network and the function it will try to approximate.

A fully-connected network with ReLU activations will create a piecewise linear function. For a network with a single layer, the maximum number of segments the network can create is 1 + the width of the layer. For instance, if you enter "3" into the hidden layers your network can have up to 4 segments.

If you add another hidden layer afterwards, it multiplies the max number of segments. So "3 3" becomes 4x4=16. After training, the network rarely achieves the maximum though.

Here is the demo: https://blog.lukesalamone.com/posts/can-a-neural-net-learn

submitted by /u/microscope1024
[link] [comments]

Read on the original site

Open the publisher's page for the full experience

View original article →

Tagged with

#neural network#machine learning#architecture#ReLU#activations#fully-connected#hidden layer#training#segments#piecewise linear#function approximation#width#demo#network layers#interactive demo#function#max number#learning#segments multiplication