👉 The hard project, often referred to as Project Gutenberg's "Hard Project" or more broadly in the context of deep learning and natural language processing, involves training a model to understand and generate human-like text from extremely limited or noisy data. This challenge is particularly daunting because the model must learn to infer meaning and context from sparse examples, which can lead to significant errors if the training data is incomplete or contains errors. The project requires sophisticated techniques to handle ambiguity, maintain coherence over long passages, and generalize well to unseen text, making it a critical test of the model's ability to learn robust language representations from minimal supervision.
