- is a family of statistical learning models influenced by biological neural networks;
- is devices of interconnected “neurons” which send messages together;
- connections have numeric weights that can be tuned based on experience and make neural network adaptive to inputs and capable of learning.
For example, A neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function, the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.
At the time of building Neural Network, the developer has the option to choose activation function in the hidden as well as the output layer. Neural Network Elements are:
Input Layer: For input features, this is basically the dataset and this information is passed to the hidden layer
Hidden Layer: computations performed on this layer are not exposed. Here all the required computations are performed on input layer data/ features and outcome is transferred to the output layer
Output Layer: It is basically the resulting layer and provides results based on information learned from the neural network.
Neural Network Configuration Options:
- Number of Hidden Layers
- Number of nodes in each hidden layer
- Activation Function
- Learning Rate
- Iterations and error level