How To Train Neural Network?
How To Train Neural Network?
As we know very well one of The most important parts of deep learning is training the neural networks.
So, let's learn how it actually works.
In this article we will try to learn how a neural network gets to train. We will also learn about the feed-forward method and back propagation method in Deep Learning.
Why training is needed?
Training in deep learning is the process that helps machines to learn about the function/equation. We have to find the optimal values of the weights of a neural network to get the desired output.
To train a neural network, we use the iterative method using gradient descent. Initially we start with random initialisation of the weights. After random initialisation of the weights, we make predictions on the data with the help of forward-propagation method, then we compute the corresponding cost function C, or loss and update each weight w by an amount proportional to dC/dw, i.e., the derivative of the cost functions w.r.t. the weight. The proportionality constant is known as the learning rate.
Now we might be thinking what is learning rate?
The learning rate is a type of hyper-parameter that helps us to controls the weights of our neural network with respect to the loss gradient. It gives us an idea of how quickly the neural network updates the concepts it has learned.
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