注意下面很多链接需要访问外国网站,无奈国情如此
1. 大神的profile,最近的optimizer search, AutoAugment,device placement, MnasNet, Swish,ENAS全部参与。n久前有幸1:1过一次聊seq2seq
An Unassuming Genius: the Man behind Google’s AutoML
链接:
https://medium.com/@aifrontiers/an-unassuming-genius-the-man-behind-google-brains-automl-4ddc801f3e9b
2. Saleforce开源给结构化数据做ML的项目,支持自动选模型,自动调参,炼丹必备
Open Sourcing TransmogrifAI: Automated Machine Learning for Structured Data
链接:
https://engineering.salesforce.com/open-sourcing-transmogrifai-4e5d0e098da2
3. Netflix给Jupyter Notebook做了很多定制的项目
Beyond Interactive: Notebook Innovation at Netflix
链接:
https://medium.com/@NetflixTechBlog/notebook-innovation-591ee3221233
4. Lyft的自驾车stack,搞了那么一堆东西最后就是为了输出steering/speed control :)
5. facebook ML视频
Introducing the Facebook Field Guide to Machine Learning video series
链接:
https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series
6. Pinterest的graph convolution neural network,做图片推荐用
PinSage: A New Graph Convolutional Neural Network for Web-Scale Recommender Systems
链接:
https://medium.com/@Pinterest_Engineering/pinsage-a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems-88795a107f48
7. CNN调试技巧
Troubleshooting Convolutional Neural Networks
链接:
https://gist.github.com/zeyademam/0f60821a0d36ea44eef496633b4430fc
8. 用dask并行化特征工程
Parallelizing Feature Engineering with Dask
链接:
https://towardsdatascience.com/parallelizing-feature-engineering-with-dask-3db88aec33b7
9. 炼丹调参工具
Understanding Hyperparameters Optimization in Deep Learning Models: Concepts and Tools
链接:
https://towardsdatascience.com/understanding-hyperparameters-optimization-in-deep-learning-models-concepts-and-tools-357002a3338a
10. hyperas调keras参数
A guide to an efficient way to build neural network architectures- Part I: Hyper-parameter selection and tuning for Dense Networks using Hyperas on Fashion-MNIST
链接:
https://towardsdatascience.com/a-guide-to-an-efficient-way-to-build-neural-network-architectures-part-i-hyper-parameter-8129009f131b
11. 各种convolution的解释
An Introduction to different Types of Convolutions in Deep Learning
链接:
https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d
12. Google改进backprop,不直接使用derivative,用进化算法去搜索update,也算是AutoML
Backprop Evolution
链接:
https://arxiv.org/pdf/1808.02822.pdf
13. fastText做NLP教程
Text Classification is Your New Secret Weapon
链接:
https://medium.com/@ageitgey/text-classification-is-your-new-secret-weapon-7ca4fad15788
14. 游戏AI简介
The Total Beginner's Guide to Game AI
链接:
https://www.gamedev.net/articles/programming/artificial-intelligence/the-total-beginners-guide-to-game-ai-r4942
15. RL各种算法简介
Introduction to Various Reinforcement Learning Algorithms.
链接:
https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287
https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-part-ii-trpo-ppo-87f2c5919bb9
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