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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 2018-11-08 13:37:26


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:

  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


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/;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



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