15大领域,50篇文章,2018年应当这样学习机器学习

AI科技大本营 2018-02-03 18:08
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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:


  1. 图像处理

  2. 风格迁移

  3. 图像分类

  4. 面部识别

  5. 视频稳像

  6. 目标检测

  7. 自动驾驶

  8. 推荐系统

  9. AI游戏

  10. AI棋手

  11. AI医疗

  12. AI语音

  13. AI音乐

  14. 自然语言处理

  15. 学习预测


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:

①https://www.pyimagesearch.com/2017/09/11/object-detection-with-deep-learning-and-opencv/;②https://www.pyimagesearch.com/2016/01/04/unifying-picamera-and-cv2-videocapture-into-a-single-class-with-opencv/

③https://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:
    ①http://benanne.github.io/2014/08/05/spotify-cnns.html
    ②https://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


  • 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:
    ①https://github.com/mokemokechicken/reversi-alpha-zero
    ②https://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


整理 | 胡永波

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