Python Interview Questions

Overview

Python Interview Questions

  1. Clone the code to your computer.
  2. You need to understand the code in main.py and modify the content in if __name__ =='__main__': to execute the get_key method in Algo.
  3. If the code is executed correctly, there will be content output, otherwise there will be no output.

Tip: Currently only x86 Linux is supported, you may need to use docker to get environment support.

Tip2: The author has no money to pay for the cloud service, please try again on the 1st of next month.

Owner
ClassmateLin
ClassmateLin
Codes of the paper Deformable Butterfly: A Highly Structured and Sparse Linear Transform.

Deformable Butterfly: A Highly Structured and Sparse Linear Transform DeBut Advantages DeBut generalizes the square power of two butterfly factor matr

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A "gym" style toolkit for building lightweight Neural Architecture Search systems

A "gym" style toolkit for building lightweight Neural Architecture Search systems

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This is the official implementation for the paper "Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization" in NeurIPS 2021.

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Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Code and Experiments for ACL-IJCNLP 2021 Paper Mind Your Outliers! Investigating the Negative Impact of Outliers on Active Learning for Visual Question Answering.

Sidd Karamcheti 50 Nov 16, 2022
Predicting 10 different clothing types using Xception pre-trained model.

Predicting-Clothing-Types Predicting 10 different clothing types using Xception pre-trained model from Keras library. It is reimplemented version from

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Simple Python application to transform Serial data into OSC messages

SerialToOSC-Bridge Simple Python application to transform Serial data into OSC messages. The current purpose is to be a compatibility layer between ha

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Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 05, 2022
The Multi-Mission Maximum Likelihood framework (3ML)

PyPi Conda The Multi-Mission Maximum Likelihood framework (3ML) A framework for multi-wavelength/multi-messenger analysis for astronomy/astrophysics.

The Multi-Mission Maximum Likelihood (3ML) 62 Dec 30, 2022
Yolact-keras实例分割模型在keras当中的实现

Yolact-keras实例分割模型在keras当中的实现 目录 性能情况 Performance 所需环境 Environment 文件下载 Download 训练步骤 How2train 预测步骤 How2predict 评估步骤 How2eval 参考资料 Reference 性能情况 训练数

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SatelliteNeRF - PyTorch-based Neural Radiance Fields adapted to satellite domain

SatelliteNeRF PyTorch-based Neural Radiance Fields adapted to satellite domain.

Kai Zhang 46 Nov 20, 2022
Official PyTorch code for Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021)

Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling (HCFlow, ICCV2021) This repository is the official P

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LaneAF: Robust Multi-Lane Detection with Affinity Fields

LaneAF: Robust Multi-Lane Detection with Affinity Fields This repository contains Pytorch code for training and testing LaneAF lane detection models i

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A curated (most recent) list of resources for Learning with Noisy Labels

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The official implementation of our CVPR 2021 paper - Hybrid Rotation Averaging: A Fast and Robust Rotation Averaging Approach

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JAX-based neural network library

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ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectives

Status: Under development (expect bug fixes and huge updates) ShinRL: A Library for Evaluating RL Algorithms from Theoretical and Practical Perspectiv

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CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

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The official implementation of CVPR 2021 Paper: Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation.

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation This repository is the official implementation of CVPR 2021 paper:

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Unsupervised Foreground Extraction via Deep Region Competition

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