Neural networks guide. Unleash the power of Neural Networks: the complete guide to understanding, Implementing AI. Александр Чичулин

a feedforward neural network involves translating the mathematical concepts into code using a programming language and a deep learning framework like TensorFlow or PyTorch. By following the steps outlined above and experimenting with different configurations, you can train and utilize neural networks for a variety of tasks.

      Fine-tuning the Model

      Fine-tuning a neural network involves optimizing its performance by adjusting various aspects of the model. In this chapter, we will explore techniques for fine-tuning a neural network:

      1. Hyperparameter Tuning:

      – Hyperparameters are settings that determine the behavior of the neural network but are not learned from the data.

      – Examples of hyperparameters include learning rate, batch size, number of hidden layers, number of neurons in each layer, regularization parameters, and activation functions.

      – Fine-tuning involves systematically varying these hyperparameters and evaluating the network’s performance to find the optimal configuration.

      2. Learning Rate Scheduling:

      – The learning rate controls the step size in parameter updates during training.

      – Choosing an appropriate learning rate is crucial for convergence and preventing overshooting or getting stuck in local minima.

      – Learning rate scheduling techniques, such as reducing the learning rate over time or using adaptive methods like Adam or RMSprop, can help fine-tune the model’s performance.