Neural Networks
Neural Network is a system designed to mimic human brain and make decisions. Here among all the layers of a network and output layer makes predictions after going through humongous amount of calculations. It is a computational model that has a network architecture. This architecture is made up of artificial neurons. This structure has specific parameters through which one can modify it for performing certain tasks.
Neural Network has many layers. Each layer is specific and performs specific tasks. Layers are Input, Hidden, Output. We can add many hidden layer according to requirement based on the deepness of the problem which we are solving. If the problem includes machine to learn much more information for classification of the nearly similar type of data, Then it requires more deeper (more hidden layers) neural net to learn more patterns, features from an image to make a correct prediction. Input layer takes input pixels of an image and passes it through the hidden layer with some weights and then hidden layer multiplies them (weights and input values) then adds bias along with activation ( All these will be explained further).
combination = bias +weights * inputs
output = activation(combination).
*Activation may be anything according to our need like Sigmoid, ReLU, Tanh etc.,
Here’s how Exactly a Neural Network works:
1. Information is fed into the input layer which transfers it to the hidden layer
2. The interconnections between the two layers assign weights to each input randomly
3. A bias added to every input after weights are multiplied with them individually
4. The weighted sum is transferred to the activation function
5. The activation function determines which nodes it should fire for feature extraction
6. The model applies an application function to the output layer to deliver the output
7. Weights are adjusted, and the output is back-propagated to minimize error
Model uses a cost function to reduce the error rate. It updates weights in back propagation based on the cost function to reach minima (reduce error).
8. The model compares the output with the original result.
9. It repeats the process to improve accuracy.
This is a high level understanding of Neural networks and how it works. There are many types of neural networks differentiated based architectures, hyper parameters to perform various tasks. Convolutional Neural Networks are majorly used ones for image classification type of problems.
Find my new article based on Convolutions to know more about it.
Thank You for reading my article :)
