weight iniitializers methods in nural network

Lack of understanding of business problems One of the main reasons why AI / machine learning projects fail is because there is a  lack of understanding of business problems or opportunities . This can be due to a number of factors, such as the inexperience of the team, unrealistic expectations, or a lack of domain knowledge Lack of data Another reason AI / machine learning projects fail is because of a lack of data. This can be due to a number of factors, such as data being too expensive to acquire, data being unavailable, or privacy concerns. In many cases, it is necessary to have a large amount of data in order to train a machine learning model. Without enough data, it is difficult to build a model that generalizes well and performs well on unseen data. Many times, the analytical solutions are built with the data we have. This results in  data bias . In addition to needing a large amount of data, the data must also be of  high quality . Poor data quality can lead to ina...

  weight :        Weight is  the parameter within a neural network that transforms input data within the network's hidden layers . A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value.A weight represent the strength of connections. y ou can find the number of weights by  counting the edges in that network . Why weight is initialized in neural network? The weights of artificial neural networks must be initialized to small random numbers. This is  because this is an expectation of the stochastic optimization algorithm used to train the model, called stochastic gradient descent Why is weight initialization important? Why Initialize Weights. The aim of weight initialization is  to prevent layer activation outputs from exploding or vanishing during the course of a forward pass through a deep neural network weight initialization types : 1. zeros class 2.ones class 3.glortnormal class 4.or...