Satvik Varshney (SatvikVarshney)

SatvikVarshney

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Satvik Varshney 's repositories

PDFSuno

Converts English PDFs/Ebooks into Hindi audiobooks using NLP, TTS, Cloud technologies, enhancing content accessibility for educational purposes in underprivileged areas with poor education rates

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Elicit

An NLP-driven tool that intelligently interprets and contextualizes content within and across unstructured documents, enabling natural language queries for precise information retrieval

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GenAI-EvenMNIST

GenAI-EvenMNIST harnesses generative AI through a Variational Auto-Encoder (VAE) with convolutional layers to generate even-digit images from the MNIST dataset. This project showcases the application of AI for image creation and deep learning

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IsingModelBoltzmannMachine

Using Monte-Carlo simulated datasets, a completely transparent Boltzmann Machine trained on 1-D Ising chain data is implemented to predict model couplers in the absence of past coupler values. Methods from machine learning applied to theoretical physics are on display in this work.

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BitWiseLearner

An innovative project utilizing Recurrent Neural Networks (RNNs) to perform multiplication on binary integers. This repository showcases how deep learning can understand and execute fundamental arithmetic operations on binary data, offering insights and methodologies for similar tasks.

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MultiLabelClassifierPyTorch

This PyTorch project sorts even MNIST numbers into groups. It shows how to train a neural network with hyperparameters that can be set in JSON, prepare data, and test its performance. It shows how to add data, check the accuracy of the model, and see the loss.

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LinearRegressionFromScratch

An investigation of basic linear regression that contrasts gradient descent with analytical answers. shows off fundamental machine learning algorithms on fictitious datasets for learining purposes.

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Deep-Learning-for-Spacetime-Classification

This project applies machine learning techniques to the classification of spacetimes in the context of general relativity, specifically addressing the Petrov classification of the Weyl tensor

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