Category
basics
3 articles in this category
What are Logits in Machine Learning and Why They Matter
TLDR: Logits are the raw, unnormalized scores produced by the final layer of a neural network ā before any probability transformation. Softmax converts them to probabilities. Temperature scales them before Softmax to control output randomness. š T...
What are Hash Tables? Basics Explained
TLDR: A hash table gives you near-O(1) lookups, inserts, and deletes by using a hash function to map keys to array indices. The tradeoff: collisions (when two keys hash to the same slot) must be handled, and a full hash table must be resized. š Th...
Unlocking the Power of ML, DL, and LLM Through Real-World Use Cases
TLDR: ML, Deep Learning, and LLMs are not competing technologies ā they are a nested hierarchy. LLMs are a type of Deep Learning. Deep Learning is a subset of ML. Choosing the right layer depends on your data type, problem complexity, and available t...