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Home Neural Networks

Neural Networks From Scratch Using Python

April 10, 2020
in Neural Networks
Neural Networks From Scratch Using Python
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Neural Networks are inspired by biological neuron of Brain

Human Brain neuron

from the dendrites inputs are being transferred to cell body , then the cell body will process it then passes that using axon , this is what Biological Neuron Is .

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Artificial Neural Network

Same process like Brain Neuron

  • Inputs are passed
  • + Symbol in the cell body denotes adding them together
  • Threshold is Activation Function (We will talk that later)

How Neural Network Works ?

Steps :

  • Takes the input Values
  • Multiplies with the weight adding bias value to it
  • Forward Propagation is finished
  • now check the error
  • then change the Weight values
  • Back-propagation is Finished
  • Repeat it until error gets low as possible
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What is Activation Function ?

IF we did not use the activation function means it is equal to the Linear Regression Model ,

Non-Linear activation Function are more overly used because in real world data-set we will handle non linear data-sets a lot so that linear is not much usefull

Activation function are used in the hidden layer and output layer

Sigmoid and its Derivatives

there are many Non-linear Activation functions are available like Sigmoid , tanh , ReLU etc….

want to know more about activation function

Each Activation functions are having their own derivatives

In this Sigmoid Derivatives has been shown , Derivatives are used for updating the Weights

if your problem Regression means in the hidden layer and output layer you should not use Sigmoid you can use ReLU , Classification means Sigmoid we can use

We are having the inputs in which 0 and 1 , outputs are 0 and 1 , so for these we can use sigmoid

if you have new situation like for 1 0 0 what will be the output ?

the whole process of the neural network

Forward Propagation

Takes the input and multiplying weights with it , adding bias to it , pass it into sigmoid function y is calculated , then doing subtraction with original y to calculated y

error =original_y — calcul_y

calculated y is passed to the sigmoid derivatives stored as sd , then multiplying error and sd , and then doing the matrix multiplication and storing the value as Adju .

Backward Propagation

Then adding Adju values to the weight , the weight has been updated , weight+=adju

repeat it until the error gets low

Code : git-hub

  • In_init_ function setting up the weight randomly
  • Defining sigmoid and its derivatives
  • the Backpropagation steps in train function
  • think function just passes the values to the neural network

Randomly started weights and finally error corrected weights , then we have given iput as 1 0 0

output for it 0.9993704 nearly 1 almost right , this is how the neural networks are Working

Credit: BecomingHuman By: Salman Faroz

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