PANKAJ'S CONTEMPLATIONS

  This blog post covers following four intermediate Python features commonly used in machine learning and data science in a story telling fun way which the  reader would definitely enjoy and appreciate : 1.       List Comprehensions 2.       Lambda Functions 3.       Decorators 4.       Generators Each topic is explained with a practical example relevant to data science tasks, complete with code snippets and their corresponding outputs. Let's begin the story. In the mystical land of Pythonia, there lived a young data wizard named Lyra. She had mastered the basic spells of data manipulation but yearned to unlock more powerful incantations for her machine learning quests. One moonlit night, as she pored over ancient tomes in her tower, a shimmering book appeared on her desk: "The Intermediate Pythonist's Grimoire." As Lyra opened its pages, she was transported into a realm where code and magic intertwined. Join Lyra as she discovers four powerful spells that e

With the dawn of transformer architecture based on self attention mechanism paper published in 2017 and  OpenAI's introduction of the  first LLM in the world, " chatGPT " in late 2022,which is designed to generate human-like coherent text response based on input prompts provided by the end user, the LLM technology has taken the world by storm and it has become the most popular technology being used in the world in a very short period of time having a lot of applications in various domains and industries. Now-a-days everyone wants to learn about NLP and LLM technologies in depth. But NLP has its own technical jargon and there are  some  key terminologies which must be understood first before  diving deeper into the actual NLP and LLM understanding. This post, to start with, attempts to provide a good ,beginner friendly explanation with suitable examples, aiming for a good clarity about NLP and LLM technology's common key technical  terms, i.e., vector, vector database