A Neural Network Playground
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. 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
Data Which dataset do you want to use? Ratio of training to test data: XX% Noise: XX Batch size: XX Features Which properties do you want to feed in? Click anywhere to edit. Weight/Bias is 0.2. 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 Test loss Training loss Colors shows data, neuron and weight values. Show test data Discretize output Um, What Is a Neural Network? It’s a technique for building a computer program that learns from data. It is based very loosely on how we…
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