小红书计算机视觉算法实习生
实习兼职风控算法地点：上海状态：招聘
任职要求
1.计算机、数学、电子工程或相关专业背景，硕士研究生或以上学历 2.具备优秀的编程能力，扎实的数据结构和算法能力 3.熟悉深度学习算法，如cnn/Transformer/CLIP等 4.精通常用的深度学习框架，如TensorFlow/PyTorch等 5.做事认真负责，有良好的沟通协调能力和自驱力，能够承受一定的工作压力 6.能够长期实习6个月及以上者优先 7.有计算机视觉相关经验者优先，面向但不限于：多模态大模型、人脸识别、通用目标检测、ocr、细粒度分类、分割、Metric Learning、主体识别、质量评价、增量学习等 8.有多模态大模型、ocr、通用目标检测经验者优先
工作职责
支持公司内容安全、业务风控、信息安全的平台建设和风险管理工作。通过AI算法、大数据、情报攻防和终端安全灯多领域交叉技术建设安全风控平台，为数亿小红书用户提供安全健康的社区环境，同时保障电商、直播等业务的健康发展 1.负责用户维度方向模型的训练和基础系统性能的优化 2.利用计算机视觉，自然语言，多模态等技术，从海量内容中识别风险用户与内容 3.跟踪行业最新动态，推进新算法的落地与应用
包括英文材料
学历+
数据结构+
https://www.youtube.com/watch?v=8hly31xKli0
In this course you will learn about algorithms and data structures, two of the fundamental topics in computer science.
https://www.youtube.com/watch?v=B31LgI4Y4DQ
Learn about data structures in this comprehensive course. We will be implementing these data structures in C or C++.
https://www.youtube.com/watch?v=CBYHwZcbD-s
Data Structures and Algorithms full course tutorial java
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
Transformer+
https://huggingface.co/learn/llm-course/en/chapter1/4
Breaking down how Large Language Models work, visualizing how data flows through.
https://poloclub.github.io/transformer-explainer/
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
https://www.youtube.com/watch?v=wjZofJX0v4M
Breaking down how Large Language Models work, visualizing how data flows through.
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.
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.
OpenCV+
https://learnopencv.com/getting-started-with-opencv/
At LearnOpenCV we are on a mission to educate the global workforce in computer vision and AI.
https://opencv.org/university/free-opencv-course/
This free OpenCV course will teach you how to manipulate images and videos, and detect objects and faces, among other exciting topics in just about 3 hours.
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
OCR+
https://www.ibm.com/think/topics/optical-character-recognition
Optical character recognition (OCR) is a technology that uses automated data extraction to quickly convert images of text into a machine-readable format.
https://www.youtube.com/watch?v=or8AcS6y1xg
Optical character recognition (OCR) is sometimes referred to as text recognition.
CNN+
https://learnopencv.com/understanding-convolutional-neural-networks-cnn/
Convolutional Neural Network (CNN) forms the basis of computer vision and image processing.
[英文] CNN Explainer
https://poloclub.github.io/cnn-explainer/
Learn Convolutional Neural Network (CNN) in your browser!
https://www.deeplearningbook.org/contents/convnets.html
Convolutional networks(LeCun, 1989), also known as convolutional neuralnetworks, or CNNs, are a specialized kind of neural network for processing data.
https://www.youtube.com/watch?v=2xqkSUhmmXU
MIT Introduction to Deep Learning 6.S191: Lecture 3 Convolutional Neural Networks for Computer Vision
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