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Chitrita Goswami's Projects

2018-03-06-ibioic icon 2018-03-06-ibioic

Teaching materials for 6-7th March 2018 delivery of introductory bioinformatics training course for IBioIC.

3dunetcnn icon 3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

acml2016_bnmc icon acml2016_bnmc

Source code for Bayesian Nonparametric Multi-label Classification ACML 2016

artificial-intelligence-deep-learning-machine-learning-tutorials icon artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)

asap icon asap

ASAP : Automated Single-cell Analysis Pipeline

ascend icon ascend

R package - Analysis of Single Cell Expression, Normalisation and Differential expression (ascend)

attn2d icon attn2d

Pervasive Attention: 2D Convolutional Networks for Sequence-to-Sequence Prediction

aucell icon aucell

AUCell: score single cells with gene regulatory networks

autoimpute icon autoimpute

An autoencoder based imputation of the sparse single cell RNA-seq data.

awesome-automl-papers icon awesome-automl-papers

A curated list of automated machine learning papers, articles, tutorials, slides and projects

awesome-quantum-machine-learning icon awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

awesome-rnn icon awesome-rnn

Recurrent Neural Network - A curated list of resources dedicated to RNN

awesome-single-cell icon awesome-single-cell

List of software packages for single-cell data analysis, including RNA-seq, ATAC-seq, etc.

azimuth icon azimuth

Machine Learning-Based Predictive Modelling of CRISPR/Cas9 guide efficiency

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