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Learning....
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Learning....
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Achievement: YOLOAchievement: Pull Sharkx2

Achievements

Achievement: YOLOAchievement: Pull Sharkx2

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namanlalitnyu/README.md

Hello, folks!

😊 I am Naman Lalit, pursuing a Master's in Computer Science at New York University Courant.
🔭 I have about 2+ years of work experience as a Software Engineer at Salesforce, Naukri.com, and CRED.
💬 I am passionate about software development, data science, and machine learning.

Technologies and Tools

Python C C++ TensorFlow Keras PyTorch Pandas NumPy Git GitHub Jupyter HTML5 CSS3 Flask Javascript Node.js React.js Docker Google Cloud Platform AWS MySQL MongoDB LLM Prompt Engineering

Where to find me

Github LinkedIn Email

Get to know me more

https://namanlalitnyu.github.io

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  1. ticker-teller ticker-teller Public

    Forked from VaranRohila/ticker-teller

    Stock Prediction Tool built using LLM generated News Articles sentiments and LSTM forecasting model.

    Jupyter Notebook 3

  2. Stock.ai Stock.ai Public

    Retrieval Augmented Generation (RAG) application for recommending stock prediction research articles using Generative AI, Vector Embeddings, and Prompt Engineering, deployed on GCP's Virtual Machine.

    TypeScript 1 3

  3. Mnist-Model-Deployment-Kubernetes Mnist-Model-Deployment-Kubernetes Public

    Cloud based Flask Application for Image Classification based on MNIST model trained and deployed on Google Kubernetes Engine using Docker containers.

    Python

  4. Rust-and-OpenMP-Qualitative-Analysis Rust-and-OpenMP-Qualitative-Analysis Public

    Qualitative Analysis of OpenMP and Rust for Parallelizing Benchmark Programs on metrics like Programmability, Scalability, RunTime Overhead, and Speedup

    Makefile 1

  5. Stock-Sentiment-Causality Stock-Sentiment-Causality Public

    Financial Sentiment Influence Analysis: Unveiling Stock Movements through Granger Causality

    Jupyter Notebook

  6. Topic-Modeling-using-KMeans Topic-Modeling-using-KMeans Public

    The project employs topic modeling and clustering techniques to uncover hidden themes within a corpus of 32 documents, utilizing advanced NLP methods and embeddings.

    Jupyter Notebook