Hi everyone! 
I just published a comprehensive blog post exploring tokenization - the fundamental process that enables AI systems to understand human language. As someone passionate about NLP and always learning from this amazing community, Iโd love to get your thoughts and feedback.
Blog Post: ML Mastery
What I covered:
- The core challenges in tokenization (language variations, OOV words, efficiency at scale)
- Deep dive into different approaches: word-based, subword, and character-based tokenization
- Real-world implementation considerations and popular tools
- Why tokenization matters for modern AI systems
I tried to balance technical depth with accessibility, covering everything from basic concepts to advanced approaches like BPE and WordPiece. The post includes practical examples and discusses challenges across different languages.
Iโd appreciate feedback on:
- Technical accuracy and completeness
- Clarity of explanations
- Any important aspects I might have missed
- Suggestions for improvement or additional topics to explore
Whether youโre a seasoned NLP researcher or just starting your AI journey, Iโd love to hear your thoughts! Your insights help me improve as a writer and deepen my understanding of these fundamental concepts.
Thank You