Hyper-parameter optimization and fine-tuning are important components in the deep learning model. To accomplish the exceptional performance of any model, choosing the right model is paramount. The optimization and fine-tuning are processed in the model to achieve greater performance. Optimization techniques are the methods or approaches that train the deep neural network to produce better performance and accurate results. Optimization refers to the parameter identification of the function by minimizing or maximizing the certain loss function of the model. Stochastic gradient descent, min-batch gradient descent, gradient descent with momentum, and the Adam optimizer are the widely used optimization techniques for deep neural networks.
Gradient Descent is a popular optimization technique used to train neural networks and find the appropriate values of parameters of a function that significantly minimizes a cost function. In mini-batch gradient descent, the cost function can decrease for some iterations based on the specified set of training examples. Fine-tuning is the process of tuning the model to perform the tasks, which is similar to the task trained before. Such similar tasks are need not be trained from the beginning. Hyperparameters are the key variables to build an effective deep learning model and determine the structure and training strength of the neural network. The hyper-parameters utilized in optimization and fine-tuning are learning rate, number of hidden layers and units, dropouts, activation function, momentum, number of epochs, and batch size.