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[译] 15 大领域、50 篇文章,2018 年应当这样学习机器学习

程序师 175 阅读

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

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

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

来源:NVIDIA & UC Berkeley

2、Using Deep Learning to Create Professional-Level Photographs

来源:Google Research

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

作者:Satya Mallick

风格迁移

4、Visual Attribute Transfer through Deep Image Analogy

来源:微软研究院 & 上海交大

5、Deep Photo Style Transfer

来源:Cornell University & Adobe

6、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.

来源:Google Brain

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

来源:Mozilla

9、Background removal with deep learning

作者:Gidi Shperber

面部识别

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

作者:Aaron Jackson

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

作者:Adrian Rosebrock

12、DEAL WITH IT in Python with Face Detection

作者:Kirk Kaiser

视频稳像

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

来源:Google Research

目标检测

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

作者:Tim Anglade

15、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

作者:Dat Tran

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

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

③http://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]

作者:Sentdex

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

作者:David Brailovsky

推荐系统

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

作者:Sophia Ciocca

21、Artwork Personalization at Netflix

来源:Netflix

AI游戏

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

作者:SethBling

23、OpenAI Baselines: DQN

来源:OpenAI

24、Reinforcement Learning on Dota 2 [Part II]

来源:OpenAI

25、Creating an AI DOOM bot

作者:Abel Castilla

26、Phase-Functioned Neural Networks for Character Control

作者:Daniel Holden

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

来源:Stanford

28、Introducing: Unity Machine Learning Agents

来源:Unity

AI棋手

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

来源:Deepmind

30、AlphaGo Zero: Learning from scratch

来源:DeepMind

31、How Does DeepMind’s AlphaGo Zero Work?

作者:Siraj Raval

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

作者:Lauri Hartikka

AI医疗

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

作者:吴恩达 & Stanford ML Group

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

作者:Julian de Wit

35、Improving Palliative Care with Deep Learning

作者:吴恩达 & Stanford ML Group

36、Heart Disease Diagnosis with Deep Learning

作者:Chuck-Hou Yee

AI语音

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

来源:Google

38、Sequence Modeling with CTC

作者:Awni Hannun

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

来源:百度

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

来源:Apple

AI音乐

41、Computer evolves to generate baroque music!

作者:Cary Huang

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

作者:Jesse Engelberg

自然语言处理

43、Learning to communicate: Agents developing their own language

来源:OpenAI

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

作者:Déborah Mesquita

45、A novel approach to neural machine translation

来源: Facebook

46、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

作者:Robert Chang

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

来源:Uber

49、Using Machine Learning to make parking easier

来源:Google

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

作者:Siraj Raval

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

作者:程序师
用程序师的眼光看世界

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