Starting with seq2seq and then moving on to modern libraries like Hugging Face or TensorFlow

ChatGPT, response to “Is it better to first understand seq2seq models in-depth and then use high-level libraries like Hugging Face or TensorFlow? Is this approach similar to studying theory first and then using a library?,” August 27, 2023, OpenAI.

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Yes, your understanding is on point. Let's delve into why this sequential approach of starting with seq2seq and then moving on to modern libraries like Hugging Face or TensorFlow is recommended:

1. Foundational Knowledge: Starting with seq2seq provides foundational knowledge on how machine translation and many other NLP tasks were tackled before the rise of transformers. It's akin to learning the basics or the "manual way" of doing things before leveraging tools that automate or optimize the process.
2. Appreciation of Progression: By understanding the limitations and challenges of earlier methods like seq2seq, you gain a deeper appreciation for the advancements that modern architectures and libraries bring to the table. This perspective is invaluable for troubleshooting and innovating in the field.
3. Customizability and Debugging: Once you understand the intricacies of seq2seq and other foundational models, you're better equipped to customize, optimize, or troubleshoot models. If something goes wrong in a transformer model from Hugging Face, for instance, your foundational knowledge could help pinpoint the issue.
4. Performance Understanding: Modern libraries provide many shortcuts and optimizations. By starting with the basics, you gain an understanding of the computational and performance challenges associated with NLP tasks. This can be crucial when working on optimization or when computational resources are a constraint.
5. Versatility: There might be situations where a full-blown transformer model is overkill, or you might be working with a dataset or use-case where a simpler model like seq2seq performs sufficiently well. Having a diverse toolkit makes you versatile as a developer.
6. Better Grasp on Advanced Libraries: Libraries like Hugging Face abstract away a lot of complexity. If you jump straight into them without understanding what's happening under the hood, you might use them as a "black box", which isn't ideal for deep understanding or customization. After grappling with the intricacies of seq2seq, you'll find that you can use advanced libraries more effectively and understand their documentation and nuances more deeply.
7. Conceptual Continuity: Many concepts in the deep learning domain, especially in NLP, build upon previous ones. For instance, the attention mechanism, which is central to transformers, was first introduced to improve seq2seq models. A step-by-step learning approach ensures a smooth continuity of concepts.

To draw an analogy: It's similar to learning mathematics. One starts with basic arithmetic before moving to algebra, calculus, and more advanced topics. Each new topic builds upon the foundational knowledge of the previous ones. Similarly, starting with seq2seq before diving into advanced libraries ensures a robust and comprehensive understanding of NLP and machine translation.

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