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

1. 江骏 @ 饿了了么

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. 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. • I’m a developer, not a data scientist.
• Focus on playing TensorFlow in cloud.
江骏 @ 饿了了么

5. Keynote Video
(Jeff Dean - part)

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

11. Training Data Validation Data Test Data
Model Serving request
train
(cpu/gpu)
✔
✘

13. What is
machine learning?
Loss
Weight
Gradient Descent
Bias
Activation Function
Hidden Layer
Learning Rate

14. • 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

15. Deep Learning

16. Python
Golang
Java
…
Graph
(DAG)
C++ Core
compile run
How does TensorFlow work

17. TensorFlow High-Level APIs & Keras & ML Toolkit
Francois CholletMartin Wicke Ashish Agarwal

19. Low-Level APIs

20. High-Level APIs

26. Keras

27. Keras

29. Video Input

30. Question Input

31. Output

32. Generate the “model”, a graph

34. Wide & Deep Learning
Heng-Tze Cheng
https://www.tensorflow.org/tutorials/wide_and_deep/
research paper

40. Distributed TensorFlow
TensorFlow Ecosystem: Integrating TensorFlow with
Your Infrastructure
Derek Murray Jonathan Hseu

45. Distributed Training
Demo

47. Hands-on TensorBoard
Dandelion Mane

48. TensorBoard Demo

49. Serving Models in Production with
TensorFlow Serving
Noah Fiedel
https://github.com/tensorflow/serving

51. TensroFlow Serving
Demo

52. XLA (Accelerated Linear Algebra)
Chris Leary and Todd Wang
https://www.tensorflow.org/versions/master/experimental/xla/

58. Other Topics
Applications

59. 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

60. Google Translation App
Demo

61. 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