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GDG-Shanghai 2017 TensorFlow Summit Recap

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Jiang Jun (江骏 @ 饿了么)
Recap all the 15 topics in the very first TensorFlow Summit.
Introduce the basic machine learning knowledges.

Published in: Technology

GDG-Shanghai 2017 TensorFlow Summit Recap

  1. 1. 江骏 @ 饿了了么
  2. 2. Jeff Dean 的传说 source: http://blog.jobbole.com/51607/ • “Jeff Dean 是直接写⼆二进制机器器代码的。” • “编译器器从不不会给 Jeff Dean 警告的,Jeff Dean 会给编译器器警告的。” • “在 2000 年年末的时候,Jeff Dean 写代码的速度突然增⻓长了了 40 倍,
 原因是他把⾃自⼰己的键盘升级到了了 USB 2.0。”
  3. 3. Jeff Dean • He has
 co-designed/implemented 
 five generations of Google's • crawling • indexing • query serving systems • and co-designed/implemented major pieces of Google’s • initial advertising • AdSense for Content systems • Google's distributed computing infrastructure, including • MapReduce, BigTable, Spanner systems • protocol buffers, LevelDB, systems infrastructure for statistical machine translation • a variety of internal and external libraries and developer tools • He is currently working on large-scale distributed systems for machine learning. Leads the Google Brain team, Google's deep learning research team
  4. 4. • I’m a developer, not a data scientist. • Focus on playing TensorFlow in cloud. 江骏 @ 饿了了么
  5. 5. Keynote Video (Jeff Dean - part)
  6. 6. TensorFlow 的上⼀一代产品 DistBelief • 解决了了数据量量 > GPU 最⼤大内存的问题 • paper 中提到最⼤大的 model,达到了了
 1.7 billion parameters, utilizing 81 machines, delivering a 12x speedup. • 它的缺点: • 擅⻓长图像识别,但对其它机器器学习 model 适⽤用性差,⽽而且不不⽀支持 mobile • 维护⼤大规模系统成为负担,缺乏抽象。 https://research.google.com/pubs/pub40565.html
  7. 7. Training Data Validation Data Test Data Model Serving request train (cpu/gpu) ✔ ✘
  8. 8. What is machine learning? Loss Weight Gradient Descent Bias Activation Function Hidden Layer Learning Rate
  9. 9. • 输⼊入 • 计算 ,把结果代⼊入 activation function,得到输出值wT x + b y x 神经元 neural • 预测得准吗?Loss function 来衡量量。⽐比如,⽤用它们的距离 • ⽬目标:让 loss 值越⼩小越好 • ⽤用 Optimizer 调整 weight 和 bias
 (⽐比如⽤用 Gradient Descent) loss = (y − y)2
  10. 10. Deep Learning
  11. 11. Python Golang Java … Graph (DAG) C++ Core compile run How does TensorFlow work
  12. 12. TensorFlow High-Level APIs & Keras & ML Toolkit Francois CholletMartin Wicke Ashish Agarwal
  13. 13. Low-Level APIs
  14. 14. High-Level APIs
  15. 15. Keras
  16. 16. Keras
  17. 17. Video Input
  18. 18. Question Input
  19. 19. Output
  20. 20. Generate the “model”, a graph
  21. 21. Wide & Deep Learning Heng-Tze Cheng https://www.tensorflow.org/tutorials/wide_and_deep/ research paper
  22. 22. Distributed TensorFlow TensorFlow Ecosystem: Integrating TensorFlow with Your Infrastructure Derek Murray Jonathan Hseu
  23. 23. Distributed Training Demo
  24. 24. Hands-on TensorBoard Dandelion Mane
  25. 25. TensorBoard Demo
  26. 26. Serving Models in Production with TensorFlow Serving Noah Fiedel https://github.com/tensorflow/serving
  27. 27. TensroFlow Serving Demo
  28. 28. XLA (Accelerated Linear Algebra) Chris Leary and Todd Wang https://www.tensorflow.org/versions/master/experimental/xla/
  29. 29. Other Topics Applications
  30. 30. TensorFlow at DeepMind Daniel Visentin Brett Kuprel Skin Cancer Image Classification Mobile and Embedded TensorFlow Pete Warden TensorFlow in Medicine Retinal Imaging Doug Eck Magenta: Music and Art Generation Eugene Brevdo Sequence Models and the RNN API Lily Peng
  31. 31. Google Translation App Demo
  32. 32. http://weibo.com/jiangjun1990 https://github.com/ohmystack http://ohmystack.com/ I’m 江骏 / ohmystack @饿了了么 推荐:Udacity Deep Learning Nanodegree

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