Deep learning isn’t just about training a model—it’s about understanding how different concepts and tools intertwine to create an efficient, effective training process. In this article, we explore seven critical concepts—nn.Module subclassing, doTraining, batch_iter, valmetrics_t, tensor masking (negative and positive), Boolean indexing, and the properties of weights and biases—and how they interconnect to form the … Continue reading Deep Learning Concepts: From nn.Module to Tensor Masking