Hey everyone! I've been diving into machine learning and decided to undertake a fun little project where I created an LSTM model to determine if a word is a palindrome. I'm reaching out to see if anyone with experience in ML can provide some feedback on my approach or suggest ways to improve the model's accuracy. I did plenty of research and even wrote up a short explanation of my thought process on my toy website, which is totally ad-free. You can check it out here: https://socksthoughtshop.lol/palindrome. Also, I've shared my GitHub repository for anyone interested! I'd really love to hear your insights or suggestions, especially because I'm having a few hiccups with accuracy.
1 Answer
That's an interesting project! Have you considered looking into Mechanistic Interpretability? It's a cool area that focuses on understanding how black-box models like yours actually work. There's a challenge from the ARENA program related to detecting palindromes that might really push your understanding. Also, you might want to experiment with using a GRU instead of an LSTM; for simple tasks like this, sometimes a simpler model might perform better because it won't get caught up in overly complex representations. Adding positional encoding could also help your model learn more effectively.
Thanks a ton for the suggestions! I enjoyed tackling this quirky problem and definitely want to dive deeper into machine learning. Your insights are super helpful!