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Hi there ๐Ÿ‘‹

๐Ÿ”ญ Iโ€™m currently working on building language models for Indian languages.

from typing import List

__author__ = "Eswar Divi"
__email__ = "[email protected]"
__description__ = (
    "๐Ÿ Pythonista | ๐Ÿ”“ Open-source enthusiastic | ๐Ÿฌ Deep Learning | ๐Ÿ’ก Innovator"
)
__location__ = "๐ŸŒŽ Anytown, SomeWhere"
__github__ = "https://github.com/EswarDivi"


class Who:
    def __init__(self):
        self.author = __author__
        self.email = __email__
        self.description = __description__
        self.location = __location__
        self.github = __github__
        self.interests = [
            "๐Ÿ“ˆ Data science",
            "๐Ÿค– Machine learning",
            "๐ŸŽจ Creative coding",
            "๐ŸŽฎ Game development",
        ]
        self.skills = [
            "๐Ÿ Python",
            "โ˜• Java",
            "๐Ÿ”ฅ Firebase",
            "๐Ÿš€ Scala",
            "๐Ÿ˜ PostgreSQL",
            "๐Ÿƒ MongoDB",
        ]
    def get_interests(self) -> str:
        return " | ".join(self.interests)

    def get_skills(self) -> str:
        return " | ".join(self.skills)

    def __repr__(self):
        return f"<h1>{self.author}</h1><p>{self.description}</p><p>{self.location}</p><p>Interests: {self.get_interests()}</p><p>Skills: {self.get_skills()}</p><a href='{self.github}' target='_blank'>Check out my code on GitHub</a>"


Who()

Eswar Divi

๐Ÿ Pythonista | ๐Ÿ”“ Open-source evangelist | ๐Ÿฌ Deep Learning | ๐Ÿ’ก Innovator

๐ŸŒŽ Anytown, SomeWhere

Interests: ๐Ÿ“ˆ Data science | ๐Ÿค– Machine learning | ๐ŸŽจ Creative coding | ๐ŸŽฎ Game development

Skills: ๐Ÿ Python | โ˜• Java | ๐Ÿ”ฅ Firebase | ๐Ÿš€ Scala | ๐Ÿ˜ PostgreSQL | ๐Ÿƒ MongoDB

Check out my code on GitHub

Eswar Divi's Projects

awesome-python icon awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

blog icon blog

Public repo for HF blog posts

brandes-algorithm-betweenness-centrality icon brandes-algorithm-betweenness-centrality

The objective of this project is to execute the Brandes algorithm to calculate betweenness centrality in a graph. The Brandes algorithm is a commonly known algorithm for efficiently computing betweenness centrality in large, complex networks.

datasets icon datasets

๐Ÿค— The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

docs icon docs

Source code for the Streamlit Python library documentation

docuconverse icon docuconverse

๐Ÿค–๐Ÿ’ฌ๐Ÿ’กChat with any document using conversational AI! Our project allows you to easily ask questions and get answers from any document. Built with Langchain.

enhanced-github icon enhanced-github

:rocket: Browser extension to display size of each file, download link and copy file contents directly to the clipboard

l1-norm-problems-using-admm icon l1-norm-problems-using-admm

The Least Absolute Deviation, Huber Fitting, and Least Absolute Shrinkage and Selection Operator methods are solved using the Alternating Direction Method of Multipliers approach from a Linear Algebra standpoint, as well as for Machine Learning justification and exploration.

litgpt icon litgpt

Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

llm_training_handbook icon llm_training_handbook

An open collection of methodologies to help with successful training of large language models.

model-hub icon model-hub

Your one-stop hub for AI and Machine Learning models.

mongostreamify icon mongostreamify

MongoStreamify is a video streaming platform that uses MongoDB as its database and is built using the FastAPI framework and Jinja2 templates.

openincolab-kaggle icon openincolab-kaggle

A web tool that seamlessly opens any Jupyter notebook from GitHub in either Kaggle or Colab, making it easier for users to collaborate and run code.

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