15大领域，50篇文章，2018年应当这样学习机器学习
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：
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
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