Solved A pretty Quickly Solved B at mid time , 24 mins , It was easy Problem C While solving C, I started getting defocused from the problem solving and more concerned with the ranks. Next time won’t do that. even after solving C Now the issue with my main logic was that I made it over-complicated. Moreover during the implementation I wasn’t ... Read more 01 Mar 2025 - 1 minute read
Choked so hard. Couldn’t solve B , took too much time on finding the “elegant” solution for A What did I learn : Move on quickly STOP CARING ABOUT RANK!! Just solve A nd B quickly, warm up an hour before maybe. No pressure. Even if you choke on B , quickly solve C , cause it is solvable for sure. Question B Almost solved , just needed to ad... Read more 28 Feb 2025 - 1 minute read
Introduction Parameter-efficient fine-tuning is particularly used in the context of large-scale pre-trained models (such as in NLP), to adapt that pre-trained model to a new task without drastically increasing the number of parameters. The challenge is this: modern pre-trained models (like BERT, GPT, T5, etc.) contain hundreds of millions, if no... Read more 27 Feb 2025 - 8 minute read
Introduction Autoencoders are a class of neural networks primarily used for unsupervised learning and dimensionality reduction. The fundamental idea behind autoencoders is to encode input data into a lower-dimensional representation and then decode it back to the original data, aiming to minimize the reconstruction error. They are also used for ... Read more 26 Feb 2025 - 7 minute read
Introduction Generative Adversarial Networks (GANs) are a class of deep learning models introduced by Ian Goodfellow and his colleagues in 2014. The core idea behind GANs is to train a generator network to produce data that is indistinguishable from real data, while simultaneously training a discriminator network to differentiate between real an... Read more 26 Feb 2025 - 3 minute read