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convergence

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A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.

  • Updated Apr 26, 2019
  • Jupyter Notebook

As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.

  • Updated Aug 4, 2020
  • R

This project implements the PageRank algorithm to rank web pages by importance using two approaches: a sampling method with the Markov Chain random surfer model and an iterative method with a recursive mathematical expression.

  • Updated Jul 30, 2024
  • Jupyter Notebook

This project implements a distributed K-means clustering algorithm using a custom-built MapReduce framework. It is designed to handle potentially large datasets by distributing the clustering workload across multiple processes or machines. Uses gRPC for the communication between mapper, reducer, master

  • Updated Apr 24, 2024
  • Python

A Python math package for numerical analysis: root finding, iterative solvers & other algorithms. Bisection, Newton, Euler, RK2, RK4, Adams-Bashforth-Moulton, etc. Uses Python, NumPy, SymPy, pytest.

  • Updated May 22, 2023
  • Python

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