Artificial Neural Networks (ANNs) is 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.
Below is a demo of how ANNs can be trained to solve simple 2-dimensional classification problem. You can tweak the parameters as you like. Don't worry, you can't break it.
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.
Below is a demo of how ANNs can be trained to solve simple 2-dimensional classification problem. You can tweak the parameters as you like. Don't worry, you can't break it.
Epoch
Data
Features
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.
This demo was orginally created by Daniel Smilkov and Shan Carter, then modified by Neo Ivy Capital Management under the Apache License