Mohit Bagaria's repositories
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
covid19india-cluster
:microscope: COVID19 India Cluster Network
Predicting-Sales-on-a-Time-Series-Data
Used ARIMA Model for Forecasting.
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
AudioClassification
Audio MNIST Classification using 1D-CNN, 2D-CNN, GAN+2D-CNN, CVN+RandomForest, and LSTMs.
Automated-Resume-Screening-System
Automated Resume Screening System using Machine Learning (With Dataset)
bert
TensorFlow code and pre-trained models for BERT
BrainAnalysis
My personal approach to deal with EEG and sleep pattern detection, based on datasets provided by Dreem.
Deep_reinforcement_learning_Course
Implementations from the free course Deep Reinforcement Learning with Tensorflow
ECG-523
Cardiologist-level arrhythmia detection and classification using deep neural networks.
faker
Faker is a Python package that generates fake data for you.
fine-tuned-berts-seq
Fine-tuned Transformers compatible BERT models for Sequence Tagging
interview-coding-problems
Popular programming problems previously asked in Online Campus placement Tests
Sleep-stage-classification-1
Sleep stage classification based on Recurrent neural networks using wrist-worn device data
tensor-house
A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain