小米广告算法实习生
实习兼职地点：北京状态：招聘
任职要求
1、熟悉常用的机器学习算法，例如GBDT、LR、LTR，深度学习Wide&Deep/DeepFM等； 2、熟悉常用的特征工程方法； 3、熟练掌握Python,Scala,C++,Java中两门以上编程语言； 4、熟悉Tensorflow/PaddlePaddle等常用分布式深度学习框架，熟悉Spark/Hadoop/Hive等大数据工具； 5、责任心强，有快速学习的能力、优秀的沟通能力，有创新精神，乐于接受挑战。
工作职责
1、面向小米广告业务需求开发算法解决方案，包括广告召回、点击率预估与大规模推荐算法等； 2、负责小米广告CTR/CVR预估，优化浅层转化和深层转化模型，推动算法在各种转化场景的落地； 3、从数据中发现现有系统和算法的不足，提出改进算法并推动实现； 4、研究推荐营销核心算法，使用海量用户行为数据对用户长短期兴趣建模，并且结合业务场景进行效果改进； 5、深度学习、迁移学习、多目标学习、强化学习等模型的设计、优化、应用和业界前沿算法研究； 6、追踪业界前沿算法优化技术，结合业务特点，对前沿的算法技术进行应用与创新。
包括英文材料
机器学习+
https://www.youtube.com/watch?v=0oyDqO8PjIg
Learn about machine learning and AI with this comprehensive 11-hour course from @LunarTech_ai.
https://www.youtube.com/watch?v=i_LwzRVP7bg
Learn Machine Learning in a way that is accessible to absolute beginners.
https://www.youtube.com/watch?v=NWONeJKn6kc
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
https://www.youtube.com/watch?v=PcbuKRNtCUc
Learn about all the most important concepts and terms related to machine learning and AI.
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/
GBDT+
https://developers.google.com/machine-learning/decision-forests/intro-to-gbdt
Like bagging and boosting, gradient boosting is a methodology applied on top of another machine learning algorithm.
https://scikit-learn.org/stable/modules/ensemble.html
Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
DeepFM+
https://d2l.ai/chapter_recommender-systems/deepfm.html
DeepFM consists of an FM component and a deep component which are integrated in a parallel structure.
https://deeprs-tutorial.github.io/WWW_DNN.pdf
特征工程+
https://www.ibm.com/think/topics/feature-engineering
Feature engineering preprocesses raw data into a machine-readable format. It optimizes ML model performance by transforming and selecting relevant features.
https://www.kaggle.com/learn/feature-engineering
Better features make better models. Discover how to get the most out of your data.
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.
Scala+
C+++
https://www.learncpp.com/
LearnCpp.com is a free website devoted to teaching you how to program in modern C++.
https://www.youtube.com/watch?v=ZzaPdXTrSb8
Java+
https://www.youtube.com/watch?v=eIrMbAQSU34
Master Java – a must-have language for software development, Android apps, and more! ☕️ This beginner-friendly course takes you from basics to real coding skills.
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.
PaddlePaddle+
https://learnopencv.com/paddlepaddle/
PaddlePaddle (PArallel Distributed Deep LEarning) is an open-source deep learning framework released by Baidu in 2016.
https://www.paddlepaddle.org.cn/tutorials
本课程采用飞桨特色的「横纵式」 教学法，从易到难，学习难度逐层递进，并结合图形和案例进行讲解，力求让刚接触深度学习的读者可以快速理解。
Spark+
[英文] Learning Spark Book
https://pages.databricks.com/rs/094-YMS-629/images/LearningSpark2.0.pdf
This new edition has been updated to reflect Apache Spark’s evolution through Spark 2.x and Spark 3.0, including its expanded ecosystem of built-in and external data sources, machine learning, and streaming technologies with which Spark is tightly integrated.
Hadoop+
https://www.runoob.com/w3cnote/hadoop-tutorial.html
Hadoop 为庞大的计算机集群提供可靠的、可伸缩的应用层计算和存储支持，它允许使用简单的编程模型跨计算机群集分布式处理大型数据集，并且支持在单台计算机到几千台计算机之间进行扩展。
[英文] Hadoop Tutorial
https://www.tutorialspoint.com/hadoop/index.htm
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.
Hive+
[英文] Hive Tutorial
https://www.tutorialspoint.com/hive/index.htm
Hive is a data warehouse infrastructure tool to process structured data in Hadoop. It resides on top of Hadoop to summarize Big Data, and makes querying and analyzing easy.
https://www.youtube.com/watch?v=D4HqQ8-Ja9Y
大数据+
https://www.youtube.com/watch?v=bAyrObl7TYE
https://www.youtube.com/watch?v=H4bf_uuMC-g
With all this talk of Big Data, we got Rebecca Tickle to explain just what makes data into Big Data.
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1、面向小米广告业务需求开发算法解决方案，包括广告召回、点击率预估与大规模推荐算法等； 2、负责小米广告CTR/CVR预估，优化浅层转化和深层转化模型，推动算法在各种转化场景的落地； 3、从数据中发现现有系统和算法的不足，提出改进算法并推动实现； 4、研究推荐营销核心算法，使用海量用户行为数据对用户长短期兴趣建模，并且结合业务场景进行效果改进； 5、深度学习、迁移学习、多目标学习、强化学习等模型的设计、优化、应用和业界前沿算法研究； 6、追踪业界前沿算法优化技术，结合业务特点，对前沿的算法技术进行应用与创新。
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