“Hiring a Machine Learning engineer or Data Scientist in Silicon Valley is becoming like hiring a professional athlete. That’s how demanding it is” — The New York Times
基于深度学习识别姑息治疗患者
Stanford ML Group 建立了一个使用深度学习算法的程序，根据电子健康记录（Electronic Health Record ，EHR，包括病历、心电图、医疗影像等信息）数据确定在未来3-12个月高风险死亡的住院患者。这些病人的预警信息将发送给姑息治疗小组，这有助于姑息护理小组尽早介入、提供服务。
姑息治疗（Palliative Care ，在日本、中国台湾翻译为舒缓医学）起源于 hospice运动，最早起源于公元四世纪。根据世界卫生组织的定义，姑息治疗强调控制疼痛及患者有关症状，并对心理、社会和精神问题予以重视，目的是为病人和家属赢得最好的生活质量。
预测模型是一个 18 层的深度神经网络，输入参数为一个病人的 EHR 数据，输出为未来 3-12 个月死亡的概率。训练数据采用斯坦福医院 EHR 数据库中的历史数据，包含超过 200 万名患者的数据。EHR 数据包括患者过去 12 个月的诊断结论、治疗程序、处方和相关细节（经过脱敏和技术处理，以替代码的形式表示），所有数据被转换成 13654 维的特征向量。训练好的模型 AUROC 评分达到 0.93 ，交叉验证的平均精度为0.69 分。
对于机器学习系统来说，使用户可以根据预测结果采取行动，需要提供预测结果的详细解释，这点对于建立用户信心至关重要。Stanford 的程序可以自动生成一个报告，在病人的 EHR 数据中高亮突出对于预测结果具有重要影响因子的条目。
分类
- 图像处理 Image Manipulation
- 风格转换 Style Transfer
- 图像分类 Image Classification
- 脸部识别 Face Recognition
- 视频稳定化 Video Stabilization
- 目标检测 Object Detection
- 自动驾驶汽车 Self Driving Car
- 智能推荐 Recommendation Al
- 智能游戏 Gaming Al
- 智能下棋 Chess Al
- 智能医学 Medical Al
- 智能演说 Speech Al
- 智能音乐 Music Al
- 自然语言处理 Natural Language Processing
- 智能预测 Prediction
Mybridge AI 在 20000 篇关于创建机器学习应用的文章中挑选了前 50 名。从有实践经验的数据科学家那里学习是一个好方法，我们可以的分享中获得构建、运营机器学习应用的经验教训。50 篇文章大致可以分为 15 个主题，如下所示：
Recommended Learning
- The Beginner’s Guide to Building an Artificial Intelligence in Unity.
- Deep Learning and Computer Vision A-Z™: Learn OpenCV, SSD & GANs and create image recognition apps.
图像处理 Image Manipulation
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- Using Deep Learning to Create Professional-Level Photographs
- High Dynamic Range (HDR) Imaging using OpenCV (Python)
风格转换 Style Transfer
- Visual Attribute Transfer through Deep Image Analogy
- Deep Photo Style Transfer: A deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style
- Deep Image Prior
图像分类 Image Classification
- Feature Visualization: How neural networks build up their understanding of images
- An absolute beginner's guide to Image Classification with Neural Networks
- Background removal with deep learning
人脸识别 Face Recognition
- Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
- Eye blink detection with OpenCV, Python, and dlib
- DEAL WITH IT in Python with Face Detection
视频稳定化 Video Stabilization
目标检测 Object Detection
- How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow, Keras & React Native
- Object detection: an overview in the age of Deep Learning
- How to train your own Object Detector with TensorFlow’s Object Detector API
- Real-time object detection with deep learning and OpenCV
自动驾驶汽车 Self Driving Car
- Self-driving Grand Theft Auto V with Python : Intro [Part I] - Sentdex
- Recognizing Traffic Lights With Deep Learning: How I learned deep learning in 10 weeks and won $5,000
智能推荐 Recommendation AI
- Spotify’s Discover Weekly: How machine learning finds your new music
- Artwork Personalization at Netflix
智能游戏 Gaming AI
- MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
- OpenAI Baselines: DQN. Reproduce reinforcement learning algorithms with performance on par with published results.
- Reinforcement Learning on Dota 2 [Part II]
- Creating an AI DOOM bot
- Phase-Functioned Neural Networks for Character Control
- The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI - Stanford University
- Introducing: Unity Machine Learning Agents – Unity Blog
智能下棋 Chess AI
- Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
- AlphaGo Zero: Learning from scratch | DeepMind
- How Does DeepMind's AlphaGo Zero Work?
- A step-by-step guide to building a simple chess AI
智能医学 Medical AI
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Can you improve lung cancer detection? 2nd place solution for the Data Science Bowl 2017.
- Improving Palliative Care with Deep Learning - Andrew Ng
- Heart Disease Diagnosis with Deep Learning
智能演说 Speech AI
- Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model - Data Scientists at Google
- Sequence Modeling with CTC
- Deep Voice: Real-time Neural Text-to-Speech - Baidu Silicon Valley AI Lab
- Deep Learning for Siri’s Voice: On-device Deep Mixture Density Networks for Hybrid Unit Selection Synthesis - Apple
智能音乐 Music AI
- Computer evolves to generate baroque music!
- Make your own music with WaveNets: Making a Neural Synthesizer Instrument
自然语言处理 Natural Language Processing
- Learning to communicate: Agents developing their own language - OpenAI Research
- Big Picture Machine Learning: Classifying Text with Neural Networks and TensorFlow
- A novel approach to neural machine translation - Facebook AI Research
- How to make a racist AI without really trying
预测 Prediction
- Using Machine Learning to Predict Value of Homes On Airbnb
- Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber
- Using Machine Learning to make parking easier
- How to Predict Stock Prices Easily - Intro to Deep Learning #7
扩展阅读:《The Machine Learning Master》
- Machine Learning(一):基于 TensorFlow 实现宠物血统智能识别
- Machine Learning (二) : 宠物智能识别之 Using OpenCV with Node.js
- Machine Learning:机器学习项目
- Machine Learning:机器学习算法
- Machine Learning:机器学习书单
- Machine Learning:机器学习参考文集
- Machine Learning:机器学习技术与知识产权法
- Machine Learning:人工智能媒体报道集
- 数据可视化（三）基于 Graphviz 实现程序化绘图
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来源：oschina
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