mohit-bags

Active-Learning-on-Regression

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good general- ization ability. However, many times it is relatively easy to collect a large number of un- labeled samples, but time-consuming or expensive to label them. Active learning for re- gression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 10 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG- based driver drowsiness estimation, verified their effectiveness and robustness.

Arrhythmia-Detection

Arrhythmia Detection into three classes AFL,AFIB,NSR(Normal Sinus Rythum) using ECG/PPG data based on RR interval Extraction

AWS-SageMaker-Docker-

Now you can train and build custom ML models on AWS Sagemaker using Docker

BERT-Notebooks

Fine tuned pre-trained BERT Model for recognition of Technological and Organizations entities

Budget-Optimization

covid19india-cluster

:microscope: COVID19 India Cluster Network

DSA-questions

Fall-Detection-using-Accelerometer

Predicting-Sales-on-a-Time-Series-Data

Used ARIMA Model for Forecasting.

XAI_ECG

Code to generate ECG figures from MIT BIH arrhythmia database, train CNN models (RESNET-50) on those images and produce explainability using 3 different methods.

mohit-bags/Active-Learning-on-Regression

Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good general- ization ability. However, many times it is relatively easy to collect a large number of un- labeled samples, but time-consuming or expensive to label them. Active learning for re- gression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 10 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG- based driver drowsiness estimation, verified their effectiveness and robustness.

mohit-bags/Arrhythmia-Detection

Arrhythmia Detection into three classes AFL,AFIB,NSR(Normal Sinus Rythum) using ECG/PPG data based on RR interval Extraction

mohit-bags/AWS-SageMaker-Docker-

Now you can train and build custom ML models on AWS Sagemaker using Docker

mohit-bags/BERT-Notebooks

Fine tuned pre-trained BERT Model for recognition of Technological and Organizations entities

mohit-bags/covid19india-cluster

:microscope: COVID19 India Cluster Network

mohit-bags/Fall-Detection-using-Accelerometer

mohit-bags/Predicting-Sales-on-a-Time-Series-Data

Used ARIMA Model for Forecasting.

mohit-bags/XAI_ECG

Code to generate ECG figures from MIT BIH arrhythmia database, train CNN models (RESNET-50) on those images and produce explainability using 3 different methods.

mohit-bags/Budget-Optimization

mohit-bags/DSA-questions

mohit-bags/Advanced-Deep-Learning

This repository includes the assignments which were to be completed as part of the course Advanced Deep Learning at Ravensburg-Weingarten University of Applied Sciences

mohit-bags/AudioClassification

Audio MNIST Classification using 1D-CNN, 2D-CNN, GAN+2D-CNN, CVN+RandomForest, and LSTMs.

mohit-bags/Automated-Resume-Screening-System

Automated Resume Screening System using Machine Learning (With Dataset)

mohit-bags/Bengali-Digit-Recognition-Model

mohit-bags/bert

TensorFlow code and pre-trained models for BERT

mohit-bags/BrainAnalysis

My personal approach to deal with EEG and sleep pattern detection, based on datasets provided by Dreem.

mohit-bags/Deep-Learning

mohit-bags/deep_arrhythmias

mohit-bags/Deep_reinforcement_learning_Course

Implementations from the free course Deep Reinforcement Learning with Tensorflow

mohit-bags/ECG-523

Cardiologist-level arrhythmia detection and classification using deep neural networks.

mohit-bags/Exploring-Embeddings

mohit-bags/faker

Faker is a Python package that generates fake data for you.

mohit-bags/fine-tuned-berts-seq

Fine-tuned Transformers compatible BERT models for Sequence Tagging

mohit-bags/Iboxz-Algorithm

mohit-bags/interview-coding-problems

Popular programming problems previously asked in Online Campus placement Tests

mohit-bags/Movie-Extensive-Dataset

mohit-bags/plagarism-checker

mohit-bags/Sleep-Stage-Classification

mohit-bags/Sleep-stage-classification-1

Sleep stage classification based on Recurrent neural networks using wrist-worn device data

mohit-bags/tensor-house

A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain