Pinned Repositories
Arduino
ArpanBiswas99.github.io
Battery-State-of-Charge-Estimation
This repository contains code for estimating the State of Charge (SoC) of LG HG2 batteries using Fully Connected Network (FCN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models along with optuna based hyperparameter tuning.
KJSCE-LY-Project-thesis-template
LY Project latex report template for KJSCE (Mumbai University). It can be used directly on Overleaf.
MATLAB-Projects
Matlab projects
MNIST-neural-network-classification
This project demonstrates the training and evaluation of neural network models for the classification of handwritten digits using the MNIST dataset. It includes both a fully connected neural network and a convolutional neural network (CNN) model for the task.
PyTFT
Automated Extraction of TFT Parameters for Simulation (SPICE MOS Level 3)
Self_Driving_Cars_Specialization
Exercises from the Self-Driving Cars Specialization by the University of Toronto on Coursera
ArpanBiswas99's Repositories
ArpanBiswas99/Battery-State-of-Charge-Estimation
This repository contains code for estimating the State of Charge (SoC) of LG HG2 batteries using Fully Connected Network (FCN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models along with optuna based hyperparameter tuning.
ArpanBiswas99/Arduino
ArpanBiswas99/ArpanBiswas99.github.io
ArpanBiswas99/KJSCE-LY-Project-thesis-template
LY Project latex report template for KJSCE (Mumbai University). It can be used directly on Overleaf.
ArpanBiswas99/MATLAB-Projects
Matlab projects
ArpanBiswas99/MNIST-neural-network-classification
This project demonstrates the training and evaluation of neural network models for the classification of handwritten digits using the MNIST dataset. It includes both a fully connected neural network and a convolutional neural network (CNN) model for the task.
ArpanBiswas99/PyTFT
Automated Extraction of TFT Parameters for Simulation (SPICE MOS Level 3)
ArpanBiswas99/Self_Driving_Cars_Specialization
Exercises from the Self-Driving Cars Specialization by the University of Toronto on Coursera