This episode explores the development and improvement of a character-level language model, MakeMore, focusing on architectural complexities and practical implementation challenges. Against the backdrop of a previous simpler model, the speaker introduces a deeper architecture inspired by WaveNet, a speech synthesis model powered by deep learning, aiming for a more progressive fusion of character information. More significantly, the implementation involves creating custom modules mimicking PyTorch's functionalities, such as embedding and flattening layers, to streamline the code and improve readability. For instance, the speaker meticulously addresses issues arising from the BatchNorm layer, highlighting the importance of managing training and evaluation states to avoid bugs. The iterative process involves refining the model architecture, adjusting hyperparameters, and debugging, ultimately achieving a validation loss of 1.993. What this means for future development is a more robust and efficient approach to building complex language models, paving the way for exploring advanced techniques like dilated causal convolutions and residual connections.
