This is the output from one neuron. Hover to see it larger.
The outputs are mixed with varying weights, shown by the thickness of the lines.
Output
国外ip地址代理
Training loss
Colors shows data, neuron and weight values.
手机坚果加速器怎么用
It’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. For a more detailed introduction to neural networks, Michael Nielsen’s Neural Networks and Deep Learning is a good place to start. For a more technical overview, try Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
手机坚果加速器怎么用
Please do! We’ve open sourced it on GitHub with the hope that it can make neural networks a little more accessible and easier to learn. You’re free to use it in any way that follows our Apache License. And if you have any suggestions for additions or changes, please let us know.
We’ve also provided some controls below to enable you tailor the playground to a specific topic or lesson. Just choose which features you’d like to be visible below then save this link, or refresh the page.
手机坚果加速器怎么用
Orange and blue are used throughout the visualization in slightly different ways, but in general orange shows negative values while blue shows positive values.
In the hidden layers, the lines are colored by the weights of the connections between neurons. Blue shows a positive weight, which means the network is using that output of the neuron as given. An orange line shows that the network is assiging a negative weight.
In the output layer, the dots are colored orange or blue depending on their original values. The background color shows what the network is predicting for a particular area. The intensity of the color shows how confident that prediction is.
手机坚果加速器怎么用
We wrote a tiny neural network library
that meets the demands of this educational visualization. For real-world applications, consider the
TensorFlow library.
手机坚果加速器怎么用
This was created by Daniel Smilkov and Shan Carter.
This is a continuation of many people’s previous work — most notably Andrej Karpathy’s 国外ip地址代理
and Chris Olah’s articles about neural networks.
Many thanks also to D. Sculley for help with the original idea and to Fernanda Viégas and Martin Wattenberg and the rest of the
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