Neural Networks-not so Grey Matter

Src: DanaFoundation
Src : DanaFoundation

Artificial Neural Networks:

z = β1*x1 + β2*x2 + β3*x3 + β4*x4+b

Single Artificial Neuron

Layers of ANN:

  1. Input Layer : As going by the name its responsible for feeding the input to the network. Number of neurons in the input layer is equal to the number of inputs we are giving to our network. It is used for passing the information to the hidden layers as no computation is being performed in hidden layers
  2. Hidden Layer : Layers between the input and output layer is call hidden layer. Its responsible for processing the inputs received from input layer and find patterns in the dataset to achieve the output. There can be any number of hidden layers and the number of hidden layers are dependent upon the nature of problem we are trying to solve
  3. Output Layer : Hidden layer sends its output to the Output layer after all the processing is been done on the inputs received from input layer. Number of Neurons in the output layer depends on the problems we are trying to solve for ex: If its a regression problem then the number of neurons in the output layer is going to be one i.e. continuous value.
Fig 1.1

Optimizing weights

  1. Forward Propagation: As shown above (Fig 1.1) we have 3 inputs. So, the number of neurons in input layer are also going to be three. Now all the inputs must be multiplied with their corresponding weights and a bias must be added to them and pass it to a hidden layer where the activation function should be applied.

β = β-α*∂j/∂β


  1. Forward Pass : It implies forward propagating from the input layer to the output layer
  2. Backward Pass : It implies back propagating from the output layer to the input layer
  3. Epoch : It specifies the number of times neural network sees our whole training data. So, one epoch is equal to one Forward and Backward pass for all training examples
  4. Batch Size : It specifies the number of training samples we use in one forward and one backward pass.

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