1. Deep Learning 2. Overview of TensorFlow 3. TensorFlow in Context

1. TensorFlow in Context
1
Jiqiong QIU

2. About me
2
● Master of engineer degree in Bioinformatic and
modelization at INSA de Lyon
● PhD in Color Formulation by Statistical Learning at UTC
and BASF Coatings
(EP 2887275A1: Method and system for determining a color formula)
● Data scientist at Sfeir

3. 1. Deep Learning
2. TensorFlow
3. TensorFlow in Context
TensorFlow in Context
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4. Deep Learning1
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5. 1 Deep Learning
1.1 What is Deep Learning?
1.2 Difference between academic research and industry
application
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6. 1.1 What is deep learning
Artificial
Intelligence
Machine
Learning
Logistic,
Regression, SVM,
Neural Network
Deep Learning
CNN, LSTM,
Neural Turing
Machines
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7. 1.1 What is deep learning
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8. 1.2 Difference between academic research and
industry application
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9. 1.2 Difference between academic research and
industry application
Academic Research Industry Application
Key Point Research Application
Time Investment Long term Short term
Development Environment Stand alone IDE, Compilation tools,
Teamwork etc
Goal Interest/ publication Problem solving
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10. TensorFlow2
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11. 2 TensorFlow
2.1 Key features
2.2 Comparison with others deep learning libraries
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12. 2.1 Key features
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● Open source by Google
● Python API
● Board
● Android (SDK) Mobile
application
https://github.com/tensorflow/tensorflow

13. However, TensorFlow is very slow...
2.1 Key features
https://github.com/soumith/convnet-benchmarks
https://www.reddit.com/r/MachineLearning/comments/48gfop/tensorflow_speed_questions/
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14. 2.2 Comparison with others deep learning
libraries http://deeplearning.net/software_links/
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15. 2.2 Comparison with others deep learning
libraries
Name Language OS GPU Related Library
Theano Python Win, Lin, Mac CUDA,Opencl Lasagne,
Keras
Torch Lua, C Lin, IOS,
Android
CUDA
Caffe C++, Python,
Matlab
Lin, Win, Mac CUDA, Opencl
TensorFlow Python Lin, Mac,
Android
CUDA Keras, Skflow
mxnet Python, R,
Julia
Lin, Windows,
Mac
CUDA
https://github.com/zer0n/deepframeworks
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16. 2.2 Comparison with others deep learning
libraries
Why TensorFlow?
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17. TensorFlow in Context3
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18. 3.1 What is unique about TensorFlow?
3.2 TensorFlow with Data Science Tools
3.3 TensorFlow for Big Data
3 TensroFlow in Context
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19. 3.1 What is unique about TensorFlow
That would be crazy if it weren't
Google
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20. The author list of TensorFlow:
⬡ Jeff Dean: father of MapReduce
⬡ Ian Goodfellow: main contributor of Theano/PyLearn2
⬡ Yangqing Jia: main contributor of Caffe
⬡ and other great Google researchers and engineers.
3.1 What is unique about TensorFlow
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21. 3.2 TensorFlow with Data Science Tools
Why we need deep learning in Industry application besides
playing Go?
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22. Avoid hand-crafted features
3.2 Tensorflow with Data Science Tools
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23. No free lunch:
Deep learning applications are generally applied to massive
unstructured data.
MNIST 60k ImageNet 50 million
Yelp Restaurant
Photo Classification
230 k
3.2 Tensorflow with Data Science Tools
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24. Most used data science languages:
TensorFlow has an API in Python
Python R
Data Manipulation Pandas dplyr, data.table
Data Visualization Matplotlib ggplot2, ggvis
Machine Learning scikit-learn caret
3.2 Tensorflow with Data Science Tools
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25. Deep Learning is hard:
3.2 Tensorflow with Data Science Tools
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26. Deep learning library like keras, Skflow (based on
TensorFlow) were developed with a focus on enabling fast
experimentation.
3.2 Tensorflow with Data Science Tools
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27. No free lunch:
Deep learning applications are generally applied to massive
unstructured data.
MNIST 60k ImageNet 50 million
Yelp Restaurant
Photo Classification
230 k
3.3 Tensorflow for Big Data
GPU makes the deep learning training possible
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28. 3.3 Tensorflow for Big Data
CPU vs GPU
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29. Training on Multiple-GPU:
⬡ A single GTX 580 GPU has only 3GB of memory
⬡ GPU memory limits the maximum size of the networks that
can be trained
⬡ Training examples may be too big to fit on on GPU
3.3 Tensorflow for Big Data
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30. 1 GPU vs multiple-GPU
3.3 Tensorflow for Big Data
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31. In TensorFlow, the supported device types are CPU and GPU.
They are represented as strings. For example:
⬡ "/cpu:0": The CPU of your machine.
⬡ "/gpu:0": The GPU of your machine, if you have one.
⬡ "/gpu:1": The second GPU of your machine, etc.
Much earier than others libraries
https://www.tensorflow.org/versions/r0.7/how_tos/using_gpu/index.html
3.3 Tensorflow for Big Data
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32. 3.3 Tensorflow for Big Data GPU is not enough
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33. Speed
Ease of use
Generality
Runs Everywhere
3.3 Tensorflow for Big Data
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34. https://databricks.com/blog/2016/01/25/deep-learning-with-spark-and-tensorflow.html
Distributed Tensor Flow on Spark is published on early 2016
3.3 Tensorflow for Big Data
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35. Tensorflow in Context
Name Language OS GPU Related Library
Theano Python Win, Lin, Mac CUDA,Opencl Lasagne,
Keras
Torch Lua, C Lin, IOS,
Android
CUDA
Caffe C++, Python,
Matlab
Lin, Win, Mac CUDA, Opencl
Tensorflow Python Lin, Mac,
Android
CUDA Keras, Skflow
mxnet Python, R,
Julia
Lin, Windows,
Mac
CUDA
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36. Thank you.
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37. 37
by Jiqiong QIU
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