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Tanay Mehta's Projects

jet-data-compression icon jet-data-compression

This repository contains the Stacked Auto Encoder made in order to compress Hadron Jet Data from 4->3 Variables. Dataset provided by CERN

latex-css icon latex-css

LaTeX.css is a CSS library that makes your website look like a LaTeX document

lightning icon lightning

Deep learning framework to train, deploy, and ship AI products Lightning fast.

linear-regression-scratch-python icon linear-regression-scratch-python

About: This is a Linear Regression Model made in Python (only two external dependencies; Numpy and Pandas). This Model Predicts the house prices from the Famous Boston House Pricing Dataset. The Best thing about this model is that it is made from scratch, i.e, I have not used any High-level Libraries such as Tensorflow, Torch, Scikit-learn, etc. This makes the overall model performance much Customised and Easier.

machine-learning-interview icon machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

make-project-js icon make-project-js

This shell script creates a Basic npm installation for a typical NodeJS project. More features adding soon

mamba-chat icon mamba-chat

Mamba-Chat: A chat LLM based on the state-space model architecture 🐍

mamba-train icon mamba-train

A single repo with all scripts and utils to train / fine-tune the Mamba model with or without FIM

minigpt icon minigpt

My PyTorch Implementation of a Mini-GPT that would be able to generate text on itself.

ml icon ml

Machine learning tools in JavaScript

mlpack icon mlpack

mlpack: a scalable C++ machine learning library --

mmlm-torch icon mmlm-torch

Various Techniques for Multi-Modal Latent Space Matching in PyTorch

models icon models

Models and examples built with TensorFlow

nadl icon nadl

A small framework that can perform automatic differentiation to calculate first-order gradients of numpy arrays.

nadl-new icon nadl-new

New NADL Version built while keeping Accelerated Computing and Advanced Type-Checking in Focus.

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