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alokamgnaneswarasai

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awesome-llm-time-series

tracking papers, datasets, and models of "large language model (LLM) for time series"

basic_neural_network_architecture

Just a basic neural network architecture implemenation using pytorch

contrastive-representation

datascience-interview

Curated list of data science interview questions and answers

diabetic-retinopathy

engtotel-transalatron

English to telugu translation project

finetuning_for_ner

This repo involves fine-tuning IndicBERT and IndicNER for Named Entity Recognition task in Telugu Language

gpt2-finetuning

image-captioning

Image captioning using CNN as encdoer and Recurrent networks as decoder

language-modelling-for-indian-names

In this Project, we will implement different types of language models for modeling Indian names. There are clealry patterns in Indian names that models could learn, and we start modeling those using n-gram models, then move to neural n-gram and RNN models.

linear-regression-using-gradient-descent

Linear-regression-using-gradient-descent

minbpe

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

ml_interview_questions

Machine Learning and Computer Vision Engineer - Technical Interview Questions

mnist_using-lenet-architecture

Trained MNIST dataset using LeNet architecture

p5-finetuning

This repository contains the implementation of recommendation systems utilizing Large Language Models (LLMs) for rating prediction and sequential recommendation tasks and review summarization , explanation generation etc

pav_project

sample

just to understand how git works

translating-indian-names-to-hindi

In this Project we will perform the task of translating Indian Names to Hindi, a sequence-to-sequence modeling task, using character-level conditional language models.

word2vec-skipgram

This repository contains implementations of text classification using a Rule-Based Classifier and Bag of Words model, as well as word embeddings using the Skip-gram model of Word2Vec. It includes detailed preprocessing steps, model training, and relevant references.