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pipeline-enabling-zero-shot-classification-for-bangla-handwritten-grapheme

prediction-of-lysine-glutarylation

Prediction of Lysine Glutarylation PTM site in Protein using Peptide Sequence Evolution based Features resolving data imbalance issue

prompt-engineering-guide

:octopus: Guide and resources for prompt engineering

publaynet

python_tutorial

python tutorial

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

se-lab

Software Engineering Lab Project

sequential_model

student_project_1

Data Science Basic Python Project

tensorflow

An Open Source Machine Learning Framework for Everyone

text-generator-by-lstm

Generates shakespeare style play by LSTM (RNN) network.

thirdshield

Automated Parking occupancy detection. Creds from VisualBuffer

tinyfederatedlearning

A scheme for privacy-preserving learning on Tiny Devices.

tool-for-text-extraction-from-paper

treecluster

Efficient phylogenetic clustering of viral sequences

treelstm

Tree-structured Long Short-Term Memory networks (http://arxiv.org/abs/1503.00075)

two-stage-bengali-qc-utilizing-smart-data-balancing-technique

Question classification (QC) is the primary step of the Question Answering (QA) system. Question Classification (QC) system classifies the questions in particular classes so that Question Answering (QA) System can provide correct answers for the questions. Our system categorizes the factoid type questions asked in natural language after extracting features of the questions. We present a two stage QC system for Bengali. It utilizes one dimensional convolutional neural network for classifying questions into coarse classes in the first stage. Word2vec representation of existing words of the question corpus have been constructed and used for assisting 1D CNN. A smart data balancing technique has been employed for giving data hungry convolutional neural network the advantage of a greater number of effective samples to learn from. For each coarse class, a separate Stochastic Gradient Descent (SGD) based classifier has been used in order to differentiate among the finer classes within that coarse class. TF-IDF representation of each word has been used as feature for the SGD classifiers implemented as part of second stage classification. Experiments show the effectiveness of our proposed method for Bengali question classification.

user-friendly-bangla-writer-a-deep-learning-based-approach

Main Task : 1.User desirable word suggestion generation according to context and user input. 2.Context dependent next word suggestion independent of user input. 3.Rectifying a whole user written paragraph all together according to user input desirability and correct Bangla grammar and spelling.