Neural Networks (NN)

Description

The course covers the fundamental principles of neural network architecture, including perceptrons, activation functions, and layers. Students explore common neural network architectures such as feedforward, convolutional, and recurrent networks, understanding their applications and advantages. Through hands-on exercises and projects, participants gain practical experience in implementing neural networks using popular frameworks like TensorFlow or PyTorch, learning techniques for training, optimizing, and evaluating model performance.