Abheesht (abheesht17)

abheesht17

Geek Repo

Location:Bangalore, India

Home Page:https://abheesht17.github.io/

Twitter:@penstrokes75

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Abheesht's repositories

super-pixels

Code for our paper: Superpixel-based Domain-Knowledge Infusion in Computer Vision (https://arxiv.org/abs/2105.09448)

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keras-nlp

Industry-strength Natural Language Processing workflows with Keras

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abheesht17

My Personal Repository

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all-about-ai-residency

AI residency programs information

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applied-ml

đź“š Papers & tech blogs by companies sharing their work on data science & machine learning in production.

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biomedical

Tools for curating biomedical training data for large-scale language modeling

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caml-mimic

multilabel classification of EHR notes

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code-soup

This is a collection of algorithms and approaches used in the book adversarial deep learning

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decepticonlp

Python Library for Robustness Monitoring and Adversarial Debugging of NLP models

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DRIFT

DRIFT is a tool for Diachronic Analysis of Scientific Literature.

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From-0-to-Research-Scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

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icd-coding-benchmark

Automatic ICD coding benchmark based on the MIMIC dataset

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keras-core

A multi-backend implementation of the Keras API, with support for TensorFlow, JAX, and PyTorch.

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keras-io

Keras documentation, hosted live at keras.io

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LILLIE

OIE Triple Extractor for Database Integration

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models

Models and examples built with TensorFlow

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Navigation-Udacity-Deep-Reinforcement-Learning

First Deep Reinforcement Learning Project

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promptsource

Toolkit for collecting and applying templates of prompting instances

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ReCAM

Our repository for the code, literature review, and results on SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning

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sktime

A unified framework for machine learning with time series

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toxic-spans-detection

Repository for our code and experiments on SemEval-2021 Task-5 Toxic Spans Detection.

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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