How weight and bias are calculated in neural network?

Neural networks are a class of machine learning models that are inspired by the structure and function of the human brain. They consist of a collection of interconnected nodes, or neurons, that process and transmit information. The basic building block of a neural network is the perceptron, which takes in inputs, applies weights to those inputs, sums them, and applies an activation function to produce an output.

A neural network is typically composed of multiple layers of perceptrons, with each layer receiving input from the previous layer and passing output to the next layer. The process of passing input through the layers of a neural network is called forward propagation, and it is during this process that the weights and biases of the network are determined.

The weights and biases in a neural network are the parameters that determine the relationship between the input and output of the network. They are used to adjust the strength of the connections between neurons and to shift the output of a neuron to a desired range. The weights and biases are adjusted during the training process, which involves iteratively presenting input data to the network, computing the output of the network, and adjusting the weights and biases to reduce the error between the predicted output and the actual output.

In order to determine the weights and biases of a neural network, there are several approaches that can be used, including hand-tuning, random initialization, and optimization algorithms. The choice of approach depends on the complexity of the network and the desired performance.

Hand-tuning is a method of determining the weights and biases of a neural network through trial and error. This approach is often used for small networks with a limited number of parameters. The weights and biases are adjusted by hand until the network produces the desired output for a given input. This approach is time-consuming and may not be feasible for large or complex networks.

Random initialization is a method of determining the weights and biases of a neural network by randomly assigning values to them. This approach is often used for larger networks with a larger number of parameters. The weights and biases are initialized to random values, and the network is trained using an optimization algorithm to minimize the error between the predicted output and the actual output. This approach can be effective, but it may require a large amount of computation to converge to a solution.

Optimization algorithms are a class of methods for determining the weights and biases of a neural network by iteratively adjusting them to minimize a cost function. The cost function measures the difference between the predicted output of the network and the actual output. There are several optimization algorithms that can be used for neural network training, including gradient descent, stochastic gradient descent, and Adam.

Gradient descent is a simple optimization algorithm that adjusts the weights and biases of a neural network by moving in the direction of the negative gradient of the cost function. The gradient of the cost function is calculated with respect to each weight and bias in the network, and the weights and biases are adjusted by a small amount in the direction of the negative gradient. This process is repeated iteratively until the cost function is minimized.

Stochastic gradient descent is a variant of gradient descent that randomly samples a subset of the training data, or mini-batch, to compute the gradient of the cost function. This approach can be more computationally efficient than gradient descent, especially for large datasets, and can converge to a solution faster.

There are other methods as well, but the majority of the time, Tensorflow can handle this part.