Exercises for manual implementation of backpropagation in a two-layer MLP (Multi-Layer Perceptron) with Batch Normalization, focusing on gradient computation without PyTorch's autograd. It covers dataset creation, parameter initialization, forward and backward passes, and neural network training.
These exercises deepen understanding of backpropagation in neural networks, especially regarding efficient gradient computation across layers and Batch Normalization. I've learned to troubleshoot and optimize models by understanding backward gradient flow. I'll apply this skill to enhance machine learning models.
Join in practicing these exercises to strengthen your AI and machine learning skills with hands-on backpropagation experience.