Description: Layer-wise learning is a training approach in neural networks where each layer of the network is trained sequentially, allowing the features learned in one layer to serve as a foundation for the training of the next. This method is based on the idea that deep neural networks, composed of multiple layers, can learn hierarchical representations of data. In this process, each layer adjusts the parameters of its activation function and is optimized using backpropagation algorithms. This approach is fundamental to the development of deep learning models, as it allows networks to specialize in different levels of abstraction, from simple features in the early layers to more complex representations in the upper layers. Layer-wise learning not only improves training efficiency but also helps avoid issues like overfitting, as each layer is trained in a controlled and progressive manner. In the context of machine learning frameworks, layer-wise learning is implemented intuitively, allowing developers to build and train neural networks in a modular and scalable way.
