Bag of Words Space Complexity Optimization | Sparse Matrix vs Dictionary (Best)

In this video, you'll learn how to optimize the space complexity of the Bag of Words (BoW) representation in Natural Language Processing (NLP).

Instead of storing a large N × M sparse matrix, we'll see how using a dictionary-based sparse representation significantly reduces memory usage from O(N × M) to O(3K), where K is the number of non-zero entries.

📌 In this video:

What is the Bag of Words model? Why the BoW matrix is sparse Space complexity of the traditional matrix representation Dictionary-based sparse representation How memory usage reduces from O(N × M) to O(3K) Step-by-step example Why sparse representations are preferred in real-world NLP systems

This video is perfect for students preparing for:

Data Structures & Algorithms (DSA) Machine Learning Natural Language Processing (NLP) AI Interviews College Exams Technical Interviews