“showing up” is one created luck that a person can get unexpected experiences just by being there! It could be for a person, place, event, etc I recently came across a podcast by Raj with Sahil Bloom on content creation. I have been following Sahil on LinkedIn and Twitter for…

Moving to the scope of Knowledge augmentation from data augmentation methods The technical term for this: Retrieval Augmented Language Models Apart from the problem of fitting a model with the data, there is a problem with fitting the model with the use case! There is an area of Question Answering…

If you recollect all the unanswered thoughts and actions in stages of time, you will know that answer is within, but rather we go around to keep searching for! Many times in a day, there are situations where my messages are unanswered, my thoughts are not discussed, and my words…

Moving down the scope of scaling, syntax, grammar, data towards reasoning, understanding, resolution of common assumptions! 🧩 Looking into K-Fold cross-validations on a chatbot conversational data and I think the quantifiers we set for Natural Language Understanding might tell there is a problem with the data, but to resolve this…

There is more to consider apart from the question asked — intelligence is to perform those possibilities in finite space and finite time. We are in the phase of conversational AI, where we augment the training phrases using Large Language models, and we finetune the synthetic samples using generative or…

Can you think of any instance in your way of problem-solving that's not objective-driven? Sometimes the problem seems very complex in an environment, does our perspective towards the problem change if we end up manipulating the objectives towards the already defined solution in the same environment? Do program and process…

Entropy, information gain etc — what it means for the dataset? 😵‍💫 We will take a dataset and analyze the interpretability of the decision behind the classification of a particular target. If you are new to Decision Trees, I have attached the link to my previous work — https://medium.com/@sangeetha-venkatesan/classify-the-main-alligator-behind-the-ml-world-19a968961511 Understanding…

CLASSIFY after you get CLARIFIED about the data 🚀 The major chunk of problem-solving with machine learning involves understanding how machine learning decisions are made when classifying. (Justifying the predictions that are made 🧠) Classification has diverse applications. One of the most predominant ones in the conversational AI landscape is…

As Naval Ravikant says “Everyone should Learn Computers, so you don't get scared when something breaks”. The same thing applies to math and machine learning. Learn the mathematical intuition behind it to some extent so you don't get scared working with the algorithms. 🦹‍♂️ Deciphering the intuition! We could approach…