just some simulations

Movie recommendations using BST Transformer

The Reptile algorithm was developed by OpenAI to perform model-agnostic meta-learning. Specifically, this algorithm was designed to quickly learn to perform new tasks with minimal training (few-sho…

Fast and efficient text classification on IMDb reviews using FNet comparing performance with a standard Transformer model.

datasets used - (cifar-100 , sar -synthetic aperture radar) , These instruction sets (SSE3, SSE4.1, SSE4.2, AVX, AVX2, FMA) help improve performance on CPUs that support them.

superman

Augmenting convolutional networks with attention-based aggregation by Touvron et. al, the authors propose to set up an equivalent visualization for convnets. They propose to substitute the global …

my readme file

Quantum Qubit Visualization

GCViT: Global Context Vision Transformer paper, presented at ICML 2023 by A Hatamizadeh et al. Then, we will fine-tune the model on the Flower dataset and jellyfish dataset for image classificatio…

A* Search , Conway's game of life , Wave Function Collapse , Particle Simulation

dynamic ascii art

Real-time object tracking using Kalman Filter, Lucas-Kanade Optical Flow, and YOLOv8 with Deep SORT for motion prediction and multi-object detection.

t-distributed Stochastic Neighbor Embedding (t-SNE) for visualization and Xtreme Gradient Boosting (XGBoost) to classify (MNIST) handwritten digit images.

Image Matching

Algorithms such as Deep Q-Learning , Vanilla DQN , Rainbow DQN , Monte Carlo Simulation - yet to be updated

Explanation and Kalman filter case studies

Models : Logistic Regression, Decision Tree, Random Forest, SVM, KNN, Gradient Boosting, XGBoost, AdaBoost, Naive Bayes, MLP Neural Network.

Core Parallel Algorithms , Linear Algebra , Image Processing , Graph Algorithms , Numerical Methods , Machine Learning/Deep Learning , Optimization Techniques , CUDA-Specific Optimization Patterns