
转转图像算法工程师-26届(J14672)
校招全职地点:北京状态:招聘
任职要求
任职要求 1.计算机、数学、电子信息、模式识别等相关专业,本科及以上学历(硕士优先) 2.熟练掌握Python编程,精通PyTorch/TensorFlow等深度学习框架,熟悉图像算法全流程(从数据预处理到模型训练、部署); 3.扎实的图像算法基础:熟悉图像分类、目标检测、语义分割等经典任务,掌握CNN、AutoEncoder、Vision-Transformer等主流模型结构及改进方法; 4.了解异常检测的核心技术(如基于重构的自编码器、生成模型GAN/扩散模型、单类分类SVM等),或有图像异常检测相关课程/项目经验; 5.良好的数学基础,能阅读英文论文,具备独立分析问题与动手实验的能力。 加分项 1.有图像…
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工作职责
1.聚焦图像/视频场景下的检测算法研发,包括图像数据清洗、增强、标注优化及模型设计与迭代; 2.基于深度学习技术(如CNN、Transformer、自编码器、生成模型等),优化图像特征提取与表征学习,提升检测算法的精度与泛化能力; 3.针对图像异常检测的核心挑战(如小样本异常、类间不平衡、复杂背景干扰等),设计针对性解决方案(如数据增广策略、损失函数优化、多尺度特征融合),同时探索相关领域Zero-Shot/Few-Shot算法落地的可能性,减少样本需求;
包括英文材料
模式识别+
https://www.mathworks.com/discovery/pattern-recognition.html
Pattern recognition is the process of classifying input data into objects, classes, or categories using computer algorithms based on key features or regularities.
https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science.
学历+
Python+
https://liaoxuefeng.com/books/python/introduction/index.html
中文,免费,零起点,完整示例,基于最新的Python 3版本。
https://www.learnpython.org/
a free interactive Python tutorial for people who want to learn Python, fast.
https://www.youtube.com/watch?v=K5KVEU3aaeQ
Master Python from scratch 🚀 No fluff—just clear, practical coding skills to kickstart your journey!
https://www.youtube.com/watch?v=rfscVS0vtbw
This course will give you a full introduction into all of the core concepts in python.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
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