Top 50机器学习项目实战总结

整理 | 胡永波

根据《纽约时报》的说法,“在硅谷招募机器学习工程师、数据科学家的情形,越来越像NFL选拔职业运动员,没有苛刻的训练很难上场了。”毕竟,高达124472美元的平均年薪可不是谁想挣就能挣到的。

正如职业运动员每天都要训练一样,机器学习的日常练习也是工程师生涯得以大踏步前进的基本保障。仅2017年一年,机器学习领域总结此类实战经验的文章便已超过20000篇,该领域相关职位的热度自是可见一斑。

从中,我们筛选出50篇最好的经验和心得,囊括了机器学习在15大细分领域的各项典型应用:

  • 图像处理
  • 风格迁移
  • 图像分类
  • 面部识别
  • 视频稳像
  • 目标检测
  • 自动驾驶
  • 推荐系统
  • AI游戏
  • AI棋手
  • AI医疗
  • AI语音
  • AI音乐
  • 自然语言处理
  • 学习预测

当然,如果你只是一个刚要准备上手机器学习的新人,我们推荐你优先考虑以下两个高分实战课程:

A) AI游戏【推荐:5041;评分:4.7/5】

The Beginner’s Guide to Building an Artificial Intelligence in Unity

链接: https://www.udemy.com/artificial-intelligence-in-unity/

B) 计算机视觉【推荐:8161;评分:4.5/5】

Deep Learning and Computer Vision A-Z™: Learn OpenCV, SSD & GANs and create image recognition apps

链接: https://www.udemy.com/computer-vision-a-z/

而对具体的实战经验,接下来我们分领域一一来看:

图像处理

1、High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

GitHub: https://github.com/NVIDIA/pix2pixHD 论文: https://arxiv.org/abs/1711.11585 博客: https://tcwang0509.github.io/pix2pixHD/

来源:NVIDIA & UC Berkeley

2、Using Deep Learning to Create Professional-Level Photographs

GitHub: https://github.com/google/creatism 论文: https://arxiv.org/abs/1707.03491 博客: https://research.googleblog.com/2017/07/using-deep-learning-to-create.html

来源:Google Research

3、High Dynamic Range (HDR) Imaging using OpenCV (Python)

项目: https://www.learnopencv.com/high-dynamic-range-hdr-imaging-using-opencv-cpp-python/ 课程主页: https://courses.learnopencv.com/p/opencv-for-beginners

作者:Satya Mallick

风格迁移

4、Visual Attribute Transfer through Deep Image Analogy

GitHub: https://github.com/msracver/Deep-Image-Analogy 论文: https://arxiv.org/abs/1705.01088

来源:微软研究院 & 上海交大

5、Deep Photo Style Transfer

GitHub: https://github.com/luanfujun/deep-photo-styletransfer 论文: https://arxiv.org/abs/1703.07511

来源:Cornell University & Adobe

6、Deep Image Prior

GitHub: https://github.com/DmitryUlyanov/deep-image-prior 论文: https://arxiv.org/abs/1711.10925 博客: https://dmitryulyanov.github.io/deep_image_prior

来源:SkolTech & Yandex & Oxford University

图像分类

7、Feature Visualization: How neural networks build up their understanding of images.

论文: https://distill.pub/2017/feature-visualization/ 代码: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb 博客: https://colah.github.io/

来源:Google Brain

8、An absolute beginner’s guide to Image Classification with Neural Networks

Github【4491收藏】: https://github.com/humphd/have-fun-with-machine-learning 中文版: https://github.com/humphd/have-fun-with-machine-learning/blob/master/README_zh-tw.md

来源:Mozilla

9、Background removal with deep learning

模型: https://towardsdatascience.com/background-removal-with-deep-learning-c4f2104b3157 部署: https://medium.com/@burgalon/deploying-your-keras-model-35648f9dc5fb

作者:Gidi Shperber

面部识别

10、Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression

GitHub: https://github.com/AaronJackson/vrn 论文: https://arxiv.org/abs/1703.07834 博客: http://aaronsplace.co.uk/papers/jackson2017recon/ Demo: http://cvl-demos.cs.nott.ac.uk/vrn/

作者:Aaron Jackson

11、Eye blink detection with OpenCV, Python, and dlib

项目: https://www.pyimagesearch.com/2017/04/24/eye-blink-detection-opencv-python-dlib/ 论文: http://vision.fe.uni-lj.si/cvww2016/proceedings/papers/05.pdf

作者:Adrian Rosebrock

12、DEAL WITH IT in Python with Face Detection

GitHub: https://github.com/burningion/automatic-memes 博客: https://www.makeartwithpython.com/blog/deal-with-it-generator-face-recognition/

作者:Kirk Kaiser

视频稳像

13、Fused Video Stabilization on the Pixel 2 and Pixel 2 XL

博客: https://research.googleblog.com/2017/11/fused-video-stabilization-on-pixel-2.html 测评: https://www.dxomark.com/google-pixel-2-reviewed-sets-new-record-smartphone-camera-quality/

来源:Google Research

目标检测

14、How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow and Keras

博客: https://medium.com/@timanglade/how-hbos-silicon-valley-built-not-hotdog-with-mobile-tensorflow-keras-react-native-ef03260747f3 项目: https://github.com/kmather73/NotHotdog-Classifier

作者:Tim Anglade

15、Object detection: an overview in the age of Deep Learning

GitHub: https://github.com/tryolabs/luminoth 论文: https://tryolabs.com/blog/2017/08/30/object-detection-an-overview-in-the-age-of-deep-learning/

来源:Tryolabs

16、How to train your own Object Detector with TensorFlow’s Object Detector API

博客: https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9 数据集: https://github.com/datitran/raccoon_dataset 产品化: https://towardsdatascience.com/building-a-real-time-object-recognition-app-with-tensorflow-and-opencv-b7a2b4ebdc32 产品代码: https://github.com/datitran/object_detector_app

作者:Dat Tran

17、Real-time object detection with deep learning and OpenCV

实战: https://www.pyimagesearch.com/2017/09/18/real-time-object-detection-with-deep-learning-and-opencv/ 入门: https://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/ https://www.pyimagesearch.com/2016/01/04/unifying-picamera-and-cv2-videocapture-into-a-single-class-with-opencv/ https://www.pyimagesearch.com/2017/08/21/deep-learning-with-opencv/

作者:Adrian Rosebrock

自动驾驶

18、Self-driving Grand Theft Auto V with Python : Intro [Part I]

GitHub: https://github.com/sentdex/pygta5 视频: https://www.youtube.com/playlist?list=PLQVvvaa0QuDeETZEOy4VdocT7TOjfSA8a 博客: https://pythonprogramming.net/game-frames-open-cv-python-plays-gta-v/

作者:Sentdex

19、Recognizing Traffic Lights With Deep Learning: How I learned deep learning in 10 weeks and won $5,000

GitHub: https://github.com/davidbrai/deep-learning-traffic-lights 博客: https://medium.freecodecamp.org/recognizing-traffic-lights-with-deep-learning-23dae23287cc 相关比赛: https://www.getnexar.com/challenge-1/

作者:David Brailovsky

推荐系统

20、Spotify’s Discover Weekly: How machine learning finds your new music

实战: https://hackernoon.com/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe 演讲: https://www.youtube.com/watch?v=A259Yo8hBRs 相关博客: http://benanne.github.io/2014/08/05/spotify-cnns.html https://notes.variogr.am/2012/12/11/how-music-recommendation-works-and-doesnt-work/

作者:Sophia Ciocca

21、Artwork Personalization at Netflix

博客: https://medium.com/netflix-techblog/artwork-personalization-c589f074ad76 论文: https://arxiv.org/abs/1003.5956 原理介绍: http://highscalability.com/blog/2017/12/11/netflix-what-happens-when-you-press-play.html

来源:Netflix

AI游戏

22、MariFlow — Self-Driving Mario Kart w/Recurrent Neural Network

项目文档: https://docs.google.com/document/d/1p4ZOtziLmhf0jPbZTTaFxSKdYqE91dYcTNqTVdd6es4 视频: https://www.youtube.com/watch?v=Ipi40cb_RsI

作者:SethBling

23、OpenAI Baselines: DQN

GitHub: https://github.com/openai/baselines 项目主页: https://blog.openai.com/openai-baselines-dqn/

来源:OpenAI

24、Reinforcement Learning on Dota 2 [Part II]

博客: https://blog.openai.com/more-on-dota-2/ 视频: https://openai.com/the-international/

来源:OpenAI

25、Creating an AI DOOM bot

博客: https://www.codelitt.com/blog/doom-ai/ 工具: http://vizdoom.cs.put.edu.pl/

作者:Abel Castilla

26、Phase-Functioned Neural Networks for Character Control

博客: http://theorangeduck.com/page/phase-functioned-neural-networks-character-control 代码: http://theorangeduck.com/media/uploads/other_stuff/pfnn.zip 论文: http://theorangeduck.com/media/uploads/other_stuff/phasefunction.pdf 视频: http://theorangeduck.com/media/uploads/other_stuff/phasefunction.mov

作者:Daniel Holden

27、The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI

论文: https://arxiv.org/abs/1702.05663 视频: https://www.youtube.com/playlist?list=PLegUCwsQzmnUpPwVv8ygMa19zNnDgJ6OC

来源:Stanford

28、Introducing: Unity Machine Learning Agents

GitHub: https://github.com/Unity-Technologies/ml-agents 博客: https://blogs.unity3d.com/cn/2017/09/19/introducing-unity-machine-learning-agents/ 文档: https://github.com/Unity-Technologies/ml-agents/tree/master/docs

来源:Unity

AI棋手

29、Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

论文: https://arxiv.org/abs/1712.01815 演讲: http://ktiml.mff.cuni.cz/~bartak/ui_seminar/talks/2017ZS/KarelHa_AlphaZero.pdf 模型: https://deepmind.com/research/alphago/alphazero-resources/ 相关实现: https://github.com/mokemokechicken/reversi-alpha-zero https://web.stanford.edu/~surag/posts/alphazero.html

来源:Deepmind

30、AlphaGo Zero: Learning from scratch

博客: https://deepmind.com/blog/alphago-zero-learning-scratch/ 论文: https://deepmind.com/documents/119/agz_unformatted_nature.pdf 棋谱: http://www.alphago-games.com/

来源:DeepMind

31、How Does DeepMind’s AlphaGo Zero Work?

GitHub: https://github.com/llSourcell/alphago_demo 视频: https://www.youtube.com/watch?v=vC66XFoN4DE

作者:Siraj Raval

32、A step-by-step guide to building a simple chess AI

GitHub: https://github.com/lhartikk/simple-chess-ai 博客: https://medium.freecodecamp.org/simple-chess-ai-step-by-step-1d55a9266977 Wiki: https://chessprogramming.wikispaces.com/

作者:Lauri Hartikka

AI医疗

33、CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

项目主页: https://stanfordmlgroup.github.io/projects/chexnet/ 论文: https://arxiv.org/abs/1711.05225 博客: https://lukeoakdenrayner.wordpress.com/2017/11/18/quick-thoughts-on-chestxray14-performance-claims-and-clinical-tasks/

作者:吴恩达 & Stanford ML Group

34、Can you improve lung cancer detection? 2nd place solution for the Data Science Bowl 2017

Kaggle: https://www.kaggle.com/c/data-science-bowl-2017 GitHub: https://github.com/dhammack/DSB2017/ 博客: http://juliandewit.github.io/kaggle-ndsb2017/

作者:Julian de Wit

35、Improving Palliative Care with Deep Learning

项目主页: https://stanfordmlgroup.github.io/projects/improving-palliative-care/ 论文: https://arxiv.org/abs/1711.06402

作者:吴恩达 & Stanford ML Group

36、Heart Disease Diagnosis with Deep Learning

GitHub: https://github.com/chuckyee/cardiac-segmentation 博客: https://blog.insightdatascience.com/heart-disease-diagnosis-with-deep-learning-c2d92c27e730 文章: https://chuckyee.github.io/cardiac-segmentation/

作者:Chuck-Hou Yee

AI语音

37、Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model 

GitHub: https://github.com/Kyubyong/tacotron 论文: https://arxiv.org/abs/1703.10135 项目主页: https://google.github.io/tacotron/

来源:Google

38、Sequence Modeling with CTC

GitHub: https://github.com/awni/speech 论文: https://distill.pub/2017/ctc/

作者:Awni Hannun

39、Deep Voice: Real-time Neural Text-to-Speech

GitHub: https://github.com/israelg99/deepvoice 论文: https://arxiv.org/abs/1702.07825 博客: http://research.baidu.com/deep-voice-production-quality-text-speech-system-constructed-entirely-deep-neural-networks/

来源:百度

40、Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis

博客: https://machinelearning.apple.com/2017/08/06/siri-voices.html

来源:Apple

AI音乐

41、Computer evolves to generate baroque music!

视频: https://www.youtube.com/watch?v=SacogDL_4JU 相关博客: http://karpathy.github.io/2015/05/21/rnn-effectiveness/

作者:Cary Huang

42、Make your own music with WaveNets: Making a Neural Synthesizer Instrument

GitHub: https://github.com/tensorflow/magenta/tree/master/magenta/models/nsynth 论文: https://arxiv.org/abs/1704.01279 博客: https://magenta.tensorflow.org/nsynth-instrument

作者:Jesse Engelberg

自然语言处理

43、Learning to communicate: Agents developing their own language

博客: https://blog.openai.com/learning-to-communicate/ 论文: https://arxiv.org/abs/1703.04908

来源:OpenAI

44、Big Picture Machine Learning: Classifying Text with Neural Networks and TensorFlow

GitHub: https://github.com/dmesquita/understanding_tensorflow_nn 博客: https://medium.freecodecamp.org/big-picture-machine-learning-classifying-text-with-neural-networks-and-tensorflow-d94036ac2274

作者:Déborah Mesquita

45、A novel approach to neural machine translation 

GitHub: https://github.com/facebookresearch/fairseq 论文: https://arxiv.org/abs/1705.03122 博客: https://code.facebook.com/posts/1978007565818999/a-novel-approach-to-neural-machine-translation

来源: Facebook

46、How to make a racist AI without really trying

Jupyter Python: https://gist.github.com/rspeer/ef750e7e407e04894cb3b78a82d66aed 博客: https://blog.conceptnet.io/2017/07/13/how-to-make-a-racist-ai-without-really-trying/

作者:Rob Speer

学习预测

47、Using Machine Learning to Predict Value of Homes On Airbnb

博客: https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d 中文: https://github.com/xitu/gold-miner/blob/master/TODO/using-machine-learning-to-predict-value-of-homes-on-airbnb.md

作者:Robert Chang

48、Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

论文: https://arxiv.org/abs/1709.01907 博客: https://eng.uber.com/neural-networks-uncertainty-estimation/

来源:Uber

49、Using Machine Learning to make parking easier

博客: https://research.googleblog.com/2017/02/using-machine-learning-to-predict.html 产品介绍: https://blog.google/products/maps/know-you-go-parking-difficulty-google-maps/

来源:Google

50、How to Predict Stock Prices Easily — Intro to Deep Learning #7

视频: https://www.youtube.com/watch?v=ftMq5ps503w 说明: https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo GitHub: https://github.com/erilyth/DeepLearning-Challenges/tree/master/Image_Classifier

作者:Siraj Raval

原文链接: https://github.com/Mybridge/learn-machine-learning https://medium.mybridge.co/learn-to-build-a-machine-learning-application-from-top-articles-of-2017-cdd5638453fc

原文发布于微信公众号 - 人工智能头条(AI_Thinker)

原文发表时间:2018-01-30

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