This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of lectures and exercises

Overview

2021-Deep-learning

This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of paper and exercises. The first folder is named Paper. The papers I have read before the graduate level have not been well organized and retained. I want to update the classic papers on classification, detection, and segmentation from 2012 to the present(Theory and realization). The second folder is named Demo that contains practical and theoretical tutorials for hands-on deep learning. The third folder is the install tutorial series, which contains the installation methods of the most popular deep learning framework Pytorch.

Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".

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PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

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An easy way to build PyTorch datasets. Modularly build datasets and automatically cache processed results

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[ICCV 2021] Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose Estimation

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Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

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Salesforce 165 Dec 21, 2022
This is my research project for the Irving Center for Cancer Dynamics/Azizi Lab, Columbia University.

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