Types of Optimization Algorithms used in Neural Networks and Ways to Optimize Gradient Descent

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Have you ever wondered which optimization algorithm to use for your Neural network Model to produce slightly better and faster results by updating the Model parameters such as Weights and Bias values . Should we use Gradient Descent or Stochastic gradient Descent or Adam? I too didn't know about the major differences between these different types of Optimization Strategies and which one is better over another before writing this article. Optimization algorithms helps us to minimize (or maximize) a Loss function (another name for Error function) E(x) which is simply a mathematical function dependent on the Model's internal parameters which are used in computing the target values(Y) from the set of predictors(X) used in the model. For example -- we call the Weights(W) and the Bias(b) values of the Neural Network as its internal parameters which are used in computing the Output values and play a major role in the training process of the Neural Network Model .

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